
🧪 Reactive Agents Quality & Efficiency Test Suite
   Provider: gemini | Model: gemini-2.5-flash
   Running 33 tests...

  ⊙ [efficiency  ] Simple math: 2+2                              ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with .withReactiveIntelligence({ telemetry: false })
  00:00:31.018 INFO  Execution started {"taskId":"01KNN67Z87J70ZYXS24W86GPTF","agentId":"test-simple-math--2-2-1775606430874"}
  00:00:31.046 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 36ms
  00:00:31.049 INFO  ◉ [strategy]   reactive
  00:00:32.072 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is 2+2?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is 2+2?
    ── raw response ──
    2+2 is 4.
  00:00:32.078 INFO  ◉ [think]      1 steps | 154 tok | 0.0s
  00:00:32.093 INFO  Execution completed {"taskId":"01KNN67Z87J70ZYXS24W86GPTF","success":true,"tokensUsed":154,"cost":0.000026249999999999998,"duration":1083}
  00:00:32.093 INFO  ◉ [complete]   ✓ 01KNN67Z87J70ZYXS24W86GPTF | 154 tok | $0.0000 | 1.1s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:31.018 INFO  Execution started {"taskId":"01KNN67Z87J70ZYXS24W86GPTF","agentId":"test-simple-math--2-2-1775606430874"}
  00:00:31.046 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 36ms
  00:00:31.049 INFO  ◉ [strategy]   reactive
  00:00:32.072 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is 2+2?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is 2+2?
    ── raw response ──
    2+2 is 4.
  00:00:32.078 INFO  ◉ [think]      1 steps | 154 tok | 0.0s
  00:00:32.093 INFO  Execution completed {"taskId":"01KNN67Z87J70ZYXS24W86GPTF","success":true,"tokensUsed":154,"cost":0.000026249999999999998,"duration":1083}
  00:00:32.093 INFO  ◉ [complete]   ✓ 01KNN67Z87J70ZYXS24W86GPTF | 154 tok | $0.0000 | 1.1s

═══ Spans (9) ═══
  ✓ execution.run (1084.9ms) [89e08fce…]
    ✓ execution.phase.bootstrap (26.1ms) [89e08fce…]
      ✓ phase.bootstrap.metrics (0.1ms) [89e08fce…]
    ✓ execution.phase.strategy-select (2.2ms) [89e08fce…]
      ✓ phase.strategy-select.metrics (0.0ms) [89e08fce…]
    ✓ execution.phase.think (1028.0ms) [89e08fce…]
      ✓ phase.think.metrics (0.0ms) [89e08fce…]
    ✓ execution.phase.complete (1.6ms) [89e08fce…]
      ✓ phase.complete.metrics (0.0ms) [89e08fce…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.1s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 154 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           25ms
├─ ✅  [strategy-select]      2ms
├─ ✅  [think]               1.0s (1 iter, 97% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.1s (1 iters, 154 tok)
  ⊙ [efficiency  ] Simple factual: capital of France             ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:32.169 INFO  Execution started {"taskId":"01KNN680D30M8HPGF08AJ5D9SK","agentId":"test-simple-factual--capital-of-france-1775606432103"}
  00:00:32.172 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:00:32.174 INFO  ◉ [strategy]   reactive
  00:00:32.849 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is the capital of France?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the capital of France?
    ── raw response ──
    The capital of France is Paris.
  00:00:32.852 INFO  ◉ [think]      1 steps | 154 tok | 0.0s
  00:00:32.865 INFO  Execution completed {"taskId":"01KNN680D30M8HPGF08AJ5D9SK","success":true,"tokensUsed":154,"cost":0.000026249999999999998,"duration":696}
  00:00:32.865 INFO  ◉ [complete]   ✓ 01KNN680D30M8HPGF08AJ5D9SK | 154 tok | $0.0000 | 0.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:32.169 INFO  Execution started {"taskId":"01KNN680D30M8HPGF08AJ5D9SK","agentId":"test-simple-factual--capital-of-france-1775606432103"}
  00:00:32.172 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:00:32.174 INFO  ◉ [strategy]   reactive
  00:00:32.849 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is the capital of France?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the capital of France?
    ── raw response ──
    The capital of France is Paris.
  00:00:32.852 INFO  ◉ [think]      1 steps | 154 tok | 0.0s
  00:00:32.865 INFO  Execution completed {"taskId":"01KNN680D30M8HPGF08AJ5D9SK","success":true,"tokensUsed":154,"cost":0.000026249999999999998,"duration":696}
  00:00:32.865 INFO  ◉ [complete]   ✓ 01KNN680D30M8HPGF08AJ5D9SK | 154 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (696.5ms) [8b828220…]
    ✓ execution.phase.bootstrap (1.9ms) [8b828220…]
      ✓ phase.bootstrap.metrics (0.0ms) [8b828220…]
    ✓ execution.phase.strategy-select (1.4ms) [8b828220…]
      ✓ phase.strategy-select.metrics (0.0ms) [8b828220…]
    ✓ execution.phase.think (677.8ms) [8b828220…]
      ✓ phase.think.metrics (0.0ms) [8b828220…]
    ✓ execution.phase.complete (1.2ms) [8b828220…]
      ✓ phase.complete.metrics (0.0ms) [8b828220…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 696ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 154 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              677ms (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 704ms (1 iters, 154 tok)
  ⊙ [efficiency  ] Simple factual: no reasoning overhead         ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:32.933 INFO  Execution started {"taskId":"01KNN68150D7NDN66X34AT84QT","agentId":"test-simple-factual--no-reasoning-overhead-1775606432869"}
  00:00:32.936 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:00:32.938 INFO  ◉ [strategy]   reactive
  00:00:33.607 DEBUG   ┄ [llm]    gemini-2.5-flash | 101 tok | end_turn | 0.7s
  00:00:33.607 DEBUG   ┄ [ctx]    2 msgs | ~101 tok used
  00:00:33.620 INFO    ┄ [1/10] [thought] Here are three programming languages:

1.  Python
2.  Java
3.  C++
  00:00:33.621 INFO    ✓ Iter 1: 101 tok, no tools — final-answer
  00:00:33.623 INFO  Execution completed {"taskId":"01KNN68150D7NDN66X34AT84QT","success":true,"tokensUsed":101,"cost":0.00002505,"duration":690}
  00:00:33.623 INFO  ◉ [complete]   ✓ 01KNN68150D7NDN66X34AT84QT | 101 tok | $0.0000 | 0.7s

═══ Logs (9) ═══
  00:00:32.933 INFO  Execution started {"taskId":"01KNN68150D7NDN66X34AT84QT","agentId":"test-simple-factual--no-reasoning-overhead-1775606432869"}
  00:00:32.936 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:00:32.938 INFO  ◉ [strategy]   reactive
  00:00:33.607 DEBUG   ┄ [llm]    gemini-2.5-flash | 101 tok | end_turn | 0.7s
  00:00:33.607 DEBUG   ┄ [ctx]    2 msgs | ~101 tok used
  00:00:33.620 INFO    ┄ [1/10] [thought] Here are three programming languages:

1.  Python
2.  Java
3.  C++
  00:00:33.621 INFO    ✓ Iter 1: 101 tok, no tools — final-answer
  00:00:33.623 INFO  Execution completed {"taskId":"01KNN68150D7NDN66X34AT84QT","success":true,"tokensUsed":101,"cost":0.00002505,"duration":690}
  00:00:33.623 INFO  ◉ [complete]   ✓ 01KNN68150D7NDN66X34AT84QT | 101 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (690.4ms) [9645e013…]
    ✓ execution.phase.bootstrap (1.7ms) [9645e013…]
      ✓ phase.bootstrap.metrics (0.0ms) [9645e013…]
    ✓ execution.phase.strategy-select (2.1ms) [9645e013…]
      ✓ phase.strategy-select.metrics (0.0ms) [9645e013…]
    ✓ execution.phase.think (681.0ms) [9645e013…]
      ✓ phase.think.metrics (0.0ms) [9645e013…]
    ✓ execution.phase.complete (2.1ms) [9645e013…]
  ✓ phase.complete.metrics (0.0ms) [9645e013…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 690ms   Steps: 2      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 101 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      2ms
├─ ✅  [think]              681ms (2 iter, 99% of time)
└─ ✅  [complete]             2ms
✓ 697ms (2 iters, 101 tok)
  ⊙ [efficiency  ] Direct answer: one-word response              ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:33.688 INFO  Execution started {"taskId":"01KNN681WHW34G78Q5RNS9VTYW","agentId":"test-direct-answer--one-word-response-1775606433627"}
  00:00:33.690 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:33.691 INFO  ◉ [strategy]   reactive
  00:00:36.511 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Is water wet? Answer yes or no.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Is water wet? Answer yes or no.
    ── raw response ──
    No.
  00:00:36.514 INFO  ◉ [think]      1 steps | 153 tok | 0.0s
  00:00:36.528 INFO  Execution completed {"taskId":"01KNN681WHW34G78Q5RNS9VTYW","success":true,"tokensUsed":153,"cost":0.00002385,"duration":2840}
  00:00:36.528 INFO  ◉ [complete]   ✓ 01KNN681WHW34G78Q5RNS9VTYW | 153 tok | $0.0000 | 2.8s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:33.688 INFO  Execution started {"taskId":"01KNN681WHW34G78Q5RNS9VTYW","agentId":"test-direct-answer--one-word-response-1775606433627"}
  00:00:33.690 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:33.691 INFO  ◉ [strategy]   reactive
  00:00:36.511 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Is water wet? Answer yes or no.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Is water wet? Answer yes or no.
    ── raw response ──
    No.
  00:00:36.514 INFO  ◉ [think]      1 steps | 153 tok | 0.0s
  00:00:36.528 INFO  Execution completed {"taskId":"01KNN681WHW34G78Q5RNS9VTYW","success":true,"tokensUsed":153,"cost":0.00002385,"duration":2840}
  00:00:36.528 INFO  ◉ [complete]   ✓ 01KNN681WHW34G78Q5RNS9VTYW | 153 tok | $0.0000 | 2.8s

═══ Spans (9) ═══
  ✓ execution.run (2842.2ms) [cf138040…]
    ✓ execution.phase.bootstrap (1.6ms) [cf138040…]
      ✓ phase.bootstrap.metrics (0.0ms) [cf138040…]
    ✓ execution.phase.strategy-select (0.9ms) [cf138040…]
      ✓ phase.strategy-select.metrics (0.0ms) [cf138040…]
    ✓ execution.phase.think (2822.8ms) [cf138040…]
      ✓ phase.think.metrics (0.0ms) [cf138040…]
    ✓ execution.phase.complete (1.0ms) [cf138040…]
      ✓ phase.complete.metrics (0.0ms) [cf138040…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 2.8s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 153 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               2.8s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 2.9s (1 iters, 153 tok)
  ⊙ [efficiency  ] Short explanation                             ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:36.596 INFO  Execution started {"taskId":"01KNN684QGZV9H5TWWE9W3BZTV","agentId":"test-short-explanation-1775606436540"}
  00:00:36.600 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:00:36.603 INFO  ◉ [strategy]   reactive
  00:00:37.508 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain what an API is in 2 sentences.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain what an API is in 2 sentences.
    ── raw response ──
    An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate with each other. It defines the methods and data formats that applications can use to request services from one another.
  00:00:37.510 INFO  ◉ [think]      1 steps | 196 tok | 0.0s
  00:00:37.522 INFO  Execution completed {"taskId":"01KNN684QGZV9H5TWWE9W3BZTV","success":true,"tokensUsed":196,"cost":0.00004875,"duration":926}
  00:00:37.522 INFO  ◉ [complete]   ✓ 01KNN684QGZV9H5TWWE9W3BZTV | 196 tok | $0.0000 | 0.9s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:36.596 INFO  Execution started {"taskId":"01KNN684QGZV9H5TWWE9W3BZTV","agentId":"test-short-explanation-1775606436540"}
  00:00:36.600 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:00:36.603 INFO  ◉ [strategy]   reactive
  00:00:37.508 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain what an API is in 2 sentences.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain what an API is in 2 sentences.
    ── raw response ──
    An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate with each other. It defines the methods and data formats that applications can use to request services from one another.
  00:00:37.510 INFO  ◉ [think]      1 steps | 196 tok | 0.0s
  00:00:37.522 INFO  Execution completed {"taskId":"01KNN684QGZV9H5TWWE9W3BZTV","success":true,"tokensUsed":196,"cost":0.00004875,"duration":926}
  00:00:37.522 INFO  ◉ [complete]   ✓ 01KNN684QGZV9H5TWWE9W3BZTV | 196 tok | $0.0000 | 0.9s

═══ Spans (9) ═══
  ✓ execution.run (926.9ms) [e9bfd7c3…]
    ✓ execution.phase.bootstrap (3.5ms) [e9bfd7c3…]
      ✓ phase.bootstrap.metrics (0.0ms) [e9bfd7c3…]
    ✓ execution.phase.strategy-select (0.9ms) [e9bfd7c3…]
      ✓ phase.strategy-select.metrics (0.0ms) [e9bfd7c3…]
    ✓ execution.phase.think (906.4ms) [e9bfd7c3…]
      ✓ phase.think.metrics (0.0ms) [e9bfd7c3…]
    ✓ execution.phase.complete (1.1ms) [e9bfd7c3…]
      ✓ phase.complete.metrics (0.0ms) [e9bfd7c3…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 926ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 196 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            3ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              906ms (1 iter, 99% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 932ms (1 iters, 196 tok)
  ⊙ [accuracy    ] Math reasoning: word problem                  ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:37.580 INFO  Execution started {"taskId":"01KNN685P8C5KGRW2WJWJ8G749","agentId":"test-math-reasoning--word-problem-1775606437526"}
  00:00:37.582 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:37.583 INFO  ◉ [strategy]   reactive
  00:00:38.676 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: A train travels at 60 mph for 2.5 hours. How far does it go?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] A train travels at 60 mph for 2.5 hours. How far does it go?
    ── raw response ──
    The train travels at 60 mph for 2.5 hours.
    To find the distance, we multiply the speed by the time.
    Distance = Speed × Time
    Distance = 60 mph × 2.5 hours
    Distance = 150 miles
    
    The train travels 150 miles.
  00:00:38.678 INFO  ◉ [think]      1 steps | 240 tok | 0.0s
  00:00:38.692 INFO  Execution completed {"taskId":"01KNN685P8C5KGRW2WJWJ8G749","success":true,"tokensUsed":240,"cost":0.00006524999999999999,"duration":1112}
  00:00:38.692 INFO  ◉ [complete]   ✓ 01KNN685P8C5KGRW2WJWJ8G749 | 240 tok | $0.0001 | 1.1s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:37.580 INFO  Execution started {"taskId":"01KNN685P8C5KGRW2WJWJ8G749","agentId":"test-math-reasoning--word-problem-1775606437526"}
  00:00:37.582 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:37.583 INFO  ◉ [strategy]   reactive
  00:00:38.676 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: A train travels at 60 mph for 2.5 hours. How far does it go?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] A train travels at 60 mph for 2.5 hours. How far does it go?
    ── raw response ──
    The train travels at 60 mph for 2.5 hours.
    To find the distance, we multiply the speed by the time.
    Distance = Speed × Time
    Distance = 60 mph × 2.5 hours
    Distance = 150 miles
    
    The train travels 150 miles.
  00:00:38.678 INFO  ◉ [think]      1 steps | 240 tok | 0.0s
  00:00:38.692 INFO  Execution completed {"taskId":"01KNN685P8C5KGRW2WJWJ8G749","success":true,"tokensUsed":240,"cost":0.00006524999999999999,"duration":1112}
  00:00:38.692 INFO  ◉ [complete]   ✓ 01KNN685P8C5KGRW2WJWJ8G749 | 240 tok | $0.0001 | 1.1s

═══ Spans (9) ═══
  ✓ execution.run (1112.8ms) [9bac08dc…]
    ✓ execution.phase.bootstrap (1.9ms) [9bac08dc…]
      ✓ phase.bootstrap.metrics (0.0ms) [9bac08dc…]
    ✓ execution.phase.strategy-select (0.8ms) [9bac08dc…]
      ✓ phase.strategy-select.metrics (0.0ms) [9bac08dc…]
    ✓ execution.phase.think (1095.1ms) [9bac08dc…]
      ✓ phase.think.metrics (0.0ms) [9bac08dc…]
    ✓ execution.phase.complete (1.0ms) [9bac08dc…]
      ✓ phase.complete.metrics (0.0ms) [9bac08dc…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.1s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 240 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               1.1s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.1s (1 iters, 240 tok)
  ⊙ [accuracy    ] Logic: syllogism                              ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:38.764 INFO  Execution started {"taskId":"01KNN686V7FNF7ZAA9APPZNBTC","agentId":"test-logic--syllogism-1775606438696"}
  00:00:38.766 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:38.781 INFO  ◉ [strategy]   reactive
  00:00:40.063 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: All roses are flowers. All flowers need water. Do roses need water? Answer with yes/no and explain briefly.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] All roses are flowers. All flowers need water. Do roses need water? Answer with yes/no and explain briefly.
    ── raw response ──
    Yes. All roses are flowers, and all flowers need water, therefore roses need water.
  00:00:40.068 INFO  ◉ [think]      1 steps | 199 tok | 0.0s
  00:00:40.083 INFO  Execution completed {"taskId":"01KNN686V7FNF7ZAA9APPZNBTC","success":true,"tokensUsed":199,"cost":0.00003795,"duration":1319}
  00:00:40.083 INFO  ◉ [complete]   ✓ 01KNN686V7FNF7ZAA9APPZNBTC | 199 tok | $0.0000 | 1.3s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:38.764 INFO  Execution started {"taskId":"01KNN686V7FNF7ZAA9APPZNBTC","agentId":"test-logic--syllogism-1775606438696"}
  00:00:38.766 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:00:38.781 INFO  ◉ [strategy]   reactive
  00:00:40.063 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: All roses are flowers. All flowers need water. Do roses need water? Answer with yes/no and explain briefly.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] All roses are flowers. All flowers need water. Do roses need water? Answer with yes/no and explain briefly.
    ── raw response ──
    Yes. All roses are flowers, and all flowers need water, therefore roses need water.
  00:00:40.068 INFO  ◉ [think]      1 steps | 199 tok | 0.0s
  00:00:40.083 INFO  Execution completed {"taskId":"01KNN686V7FNF7ZAA9APPZNBTC","success":true,"tokensUsed":199,"cost":0.00003795,"duration":1319}
  00:00:40.083 INFO  ◉ [complete]   ✓ 01KNN686V7FNF7ZAA9APPZNBTC | 199 tok | $0.0000 | 1.3s

═══ Spans (9) ═══
  ✓ execution.run (1321.6ms) [f9a2f826…]
    ✓ execution.phase.bootstrap (2.0ms) [f9a2f826…]
      ✓ phase.bootstrap.metrics (0.0ms) [f9a2f826…]
    ✓ execution.phase.strategy-select (14.9ms) [f9a2f826…]
      ✓ phase.strategy-select.metrics (0.0ms) [f9a2f826…]
    ✓ execution.phase.think (1286.1ms) [f9a2f826…]
      ✓ phase.think.metrics (0.0ms) [f9a2f826…]
    ✓ execution.phase.complete (2.5ms) [f9a2f826…]
      ✓ phase.complete.metrics (0.0ms) [f9a2f826…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.3s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 199 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]     14ms
├─ ✅  [think]               1.3s (1 iter, 99% of time)
└─ ✅  [complete]             2ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.3s (1 iters, 199 tok)
  ⊙ [accuracy    ] Code generation: fizzbuzz                     ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:40.168 INFO  Execution started {"taskId":"01KNN6886YTVM46GVHRKV7ASHE","agentId":"test-code-generation--fizzbuzz-1775606440092"}
  00:00:40.173 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 5ms
  00:00:40.175 INFO  ◉ [strategy]   reactive
  00:00:41.547 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a JavaScript function called fizzbuzz that takes a number n and returns 'Fizz' if divisible by 3, 'Buzz' if by 5, 'FizzBuzz' if both, or the number as a string. Include the complete function code in your answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a JavaScript function called fizzbuzz that takes a number n and returns 'Fizz' if divisible by 3, 'Buzz' if by 5, 'FizzBuzz' if both, or the number as a string. Include the complete function code in your answer.
    ── raw response ──
    ```javascript
    function fizzbuzz(n) {
      if (n % 3 === 0 && n % 5 === 0) {
        return 'FizzBuzz';
      } else if (n % 3 === 0) {
        return 'Fizz';
      } else if (n % 5 === 0) {
        return 'Buzz';
      } else {
        return String(n);
      }
    }
    ```
  00:00:41.550 INFO  ◉ [think]      1 steps | 340 tok | 0.0s
  00:00:41.562 INFO  Execution completed {"taskId":"01KNN6886YTVM46GVHRKV7ASHE","success":true,"tokensUsed":340,"cost":0.00009554999999999999,"duration":1394}
  00:00:41.562 INFO  ◉ [complete]   ✓ 01KNN6886YTVM46GVHRKV7ASHE | 340 tok | $0.0001 | 1.4s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:40.168 INFO  Execution started {"taskId":"01KNN6886YTVM46GVHRKV7ASHE","agentId":"test-code-generation--fizzbuzz-1775606440092"}
  00:00:40.173 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 5ms
  00:00:40.175 INFO  ◉ [strategy]   reactive
  00:00:41.547 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a JavaScript function called fizzbuzz that takes a number n and returns 'Fizz' if divisible by 3, 'Buzz' if by 5, 'FizzBuzz' if both, or the number as a string. Include the complete function code in your answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a JavaScript function called fizzbuzz that takes a number n and returns 'Fizz' if divisible by 3, 'Buzz' if by 5, 'FizzBuzz' if both, or the number as a string. Include the complete function code in your answer.
    ── raw response ──
    ```javascript
    function fizzbuzz(n) {
      if (n % 3 === 0 && n % 5 === 0) {
        return 'FizzBuzz';
      } else if (n % 3 === 0) {
        return 'Fizz';
      } else if (n % 5 === 0) {
        return 'Buzz';
      } else {
        return String(n);
      }
    }
    ```
  00:00:41.550 INFO  ◉ [think]      1 steps | 340 tok | 0.0s
  00:00:41.562 INFO  Execution completed {"taskId":"01KNN6886YTVM46GVHRKV7ASHE","success":true,"tokensUsed":340,"cost":0.00009554999999999999,"duration":1394}
  00:00:41.562 INFO  ◉ [complete]   ✓ 01KNN6886YTVM46GVHRKV7ASHE | 340 tok | $0.0001 | 1.4s

═══ Spans (9) ═══
  ✓ execution.run (1395.4ms) [401be772…]
    ✓ execution.phase.bootstrap (3.5ms) [401be772…]
      ✓ phase.bootstrap.metrics (0.0ms) [401be772…]
    ✓ execution.phase.strategy-select (2.2ms) [401be772…]
      ✓ phase.strategy-select.metrics (0.0ms) [401be772…]
    ✓ execution.phase.think (1373.9ms) [401be772…]
      ✓ phase.think.metrics (0.0ms) [401be772…]
    ✓ execution.phase.complete (1.0ms) [401be772…]
      ✓ phase.complete.metrics (0.0ms) [401be772…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.4s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 340 │
│ Cost:     ~$0.001                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            3ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               1.4s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.4s (1 iters, 340 tok)
  ⊙ [accuracy    ] Factual accuracy: no hallucination            ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:41.626 INFO  Execution started {"taskId":"01KNN689MMTGFKHSPJ26BBJFNF","agentId":"test-factual-accuracy--no-hallucination-1775606441568"}
  00:00:41.649 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 23ms
  00:00:41.650 INFO  ◉ [strategy]   reactive
  00:00:42.367 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What year was TypeScript first released by Microsoft?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What year was TypeScript first released by Microsoft?
    ── raw response ──
    TypeScript was first released by Microsoft in October 2012.
  00:00:42.370 INFO  ◉ [think]      1 steps | 165 tok | 0.0s
  00:00:42.383 INFO  Execution completed {"taskId":"01KNN689MMTGFKHSPJ26BBJFNF","success":true,"tokensUsed":165,"cost":0.00003105,"duration":757}
  00:00:42.383 INFO  ◉ [complete]   ✓ 01KNN689MMTGFKHSPJ26BBJFNF | 165 tok | $0.0000 | 0.8s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:41.626 INFO  Execution started {"taskId":"01KNN689MMTGFKHSPJ26BBJFNF","agentId":"test-factual-accuracy--no-hallucination-1775606441568"}
  00:00:41.649 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 23ms
  00:00:41.650 INFO  ◉ [strategy]   reactive
  00:00:42.367 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What year was TypeScript first released by Microsoft?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What year was TypeScript first released by Microsoft?
    ── raw response ──
    TypeScript was first released by Microsoft in October 2012.
  00:00:42.370 INFO  ◉ [think]      1 steps | 165 tok | 0.0s
  00:00:42.383 INFO  Execution completed {"taskId":"01KNN689MMTGFKHSPJ26BBJFNF","success":true,"tokensUsed":165,"cost":0.00003105,"duration":757}
  00:00:42.383 INFO  ◉ [complete]   ✓ 01KNN689MMTGFKHSPJ26BBJFNF | 165 tok | $0.0000 | 0.8s

═══ Spans (9) ═══
  ✓ execution.run (757.9ms) [e2ab36fc…]
    ✓ execution.phase.bootstrap (22.4ms) [e2ab36fc…]
      ✓ phase.bootstrap.metrics (0.0ms) [e2ab36fc…]
    ✓ execution.phase.strategy-select (0.9ms) [e2ab36fc…]
      ✓ phase.strategy-select.metrics (0.0ms) [e2ab36fc…]
    ✓ execution.phase.think (720.0ms) [e2ab36fc…]
      ✓ phase.think.metrics (0.0ms) [e2ab36fc…]
    ✓ execution.phase.complete (1.2ms) [e2ab36fc…]
      ✓ phase.complete.metrics (0.0ms) [e2ab36fc…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 757ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 165 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           22ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              720ms (1 iter, 97% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 769ms (1 iters, 165 tok)
  ⊙ [reasoning   ] ReAct: multi-step analysis                    ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:42.452 INFO  Execution started {"taskId":"01KNN68AEG77VWHGQ5SFSMJ9NC","agentId":"test-react--multi-step-analysis-1775606442395"}
  00:00:42.461 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 9ms
  00:00:42.462 INFO  ◉ [strategy]   reactive
  00:00:52.512 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Compare the pros and cons of using TypeScript vs JavaScript for a large-scale application. Consider at least: type safety, tooling, learning curve, and ecosystem. Provide a structured comparison.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Compare the pros and cons of using TypeScript vs JavaScript for a large-scale application. Consider at least: type safety, tooling, learning curve, and ecosystem. Provide a structured comparison.
    ── raw response ──
    When comparing TypeScript and JavaScript for a large-scale application, several key factors come into play. Here's a structured comparison focusing on type safety, tooling, learning curve, and ecosystem:
    
    ## TypeScript vs. JavaScript for Large-Scale Applications
    
    ### 1. Type Safety
    
    **TypeScript**
    *   **Pros:**
        *   **Enhanced Type Safety:** Provides static type checking at compile time, catching many common errors (e.g., typos, incorrect function arguments, null/undefined issues) before runtime. This significantly reduces bugs in large codebases.
        *   **Improved Code Quality:** Enforces a contract for data structures and function signatures, leading to more predictable and robust code.
        *   **Better Refactoring:** The type system makes refactoring large codebases much safer, as the compiler can highlight breaking changes.
    *   **Cons:**
        *   **Initial Overhead:** Requires developers to explicitly define types, which can feel like extra work initially.
        *   **Complexity for Advanced Types:** While powerful, advanced type definitions can sometimes be complex and require a deeper understanding of TypeScript's type system.
    
    **JavaScript**
    *   **Pros:**
        *   **Flexibility:** Its dynamic nature allows for rapid prototyping and less rigid code structure, which can be beneficial in very early stages or for smaller projects.
    *   **Cons:**
        *   **Lack of Type Safety:** Being dynamically typed, JavaScript defers type checking to runtime. This means type-related errors often manifest as runtime bugs, which are harder to debug and more costly to fix in large applications.
        *   **Reduced Predictability:** Without explicit types, it can be challenging to understand the expected data types for variables and function parameters, especially when working with unfamiliar code or in large teams.
        *   **Difficult Refactoring:** Refactoring large JavaScript codebases can be risky, as there's no compiler to catch type-related errors introduced by changes.
    
    ### 2. Tooling
    
    **TypeScript**
    *   **Pros:**
        *   **Superior IDE Support:** Due to its type information, TypeScript enables powerful features in IDEs like Visual Studio Code, WebStorm, etc. This includes:
            *   **IntelliSense/Autocompletion:** Highly accurate and context-aware suggestions.
            *   **Go-to-Definition:** Easily navigate to the source of a variable, function, or type.
            *   **Refactoring Tools:** Safer and more reliable automated refactoring.
            *   **Error Highlighting:** Real-time feedback on type errors.
        *   **Enhanced Debugging:** Type information can aid in understanding variable states during debugging.
        *   **Code Generation/Documentation:** Types can be used to generate documentation or even API clients.
    *   **Cons:**
        *   **Build Step Required:** TypeScript code needs to be transpiled into JavaScript before it can be run in browsers or Node.js environments, adding an extra step to the development workflow (though this is common with modern JavaScript anyway).
    
    **JavaScript**
    *   **Pros:**
        *   **Direct Execution:** No transpilation step is strictly required for basic JavaScript, allowing for immediate execution.
        *   **Mature Ecosystem of Linters/Formatters:** Tools like ESLint and Prettier are highly mature and widely adopted for maintaining code quality and consistency.
    *   **Cons:**
        *   **Limited IDE Support for Type-Related Features:** While IDEs provide good general JavaScript support, they cannot offer the same level of type-aware IntelliSense, refactoring, or error checking as with TypeScript.
        *   **Reliance on External Tools for Type Inference:** Tools like JSDoc comments or external type inference libraries are often used to compensate for the lack of native type information, adding complexity.
    
    ### 3. Learning Curve
    
    **TypeScript**
    *   **Pros:**
        *   **Gradual Adoption:** Can be introduced incrementally into existing JavaScript projects.
        *   **Familiar Syntax:** As a superset of JavaScript, developers familiar with JS will find most of the syntax familiar.
    *   **Cons:**
        *   **Initial Learning Overhead:** Developers new to static typing will need to learn TypeScript's type syntax, concepts (interfaces, types, generics, enums), and how to configure the compiler.
        *   **Increased Cognitive Load:** For some, managing types alongside business logic can initially feel like additional mental overhead.
        *   **Debugging Type Errors:** Understanding and resolving complex type errors can be challenging for beginners.
    
    **JavaScript**
    *   **Pros:**
        *   **Lower Barrier to Entry:** Developers can start writing code quickly without needing to learn any additional type syntax or configuration.
        *   **Flexibility:** Its dynamic nature can feel less restrictive for beginners.
    *   **Cons:**
        *   **Hidden Complexity:** While easy to start, managing complexity and avoiding runtime errors in large JavaScript projects requires significant discipline, extensive testing, and often a deep understanding of potential type pitfalls.
        *   **Harder to Onboard New Developers:** Without types, new team members might take longer to understand the expected data structures and function contracts within a large codebase.
    
    ### 4. Ecosystem
    
    **TypeScript**
    *   **Pros:**
        *   **Strong Community Adoption:** Gaining massive traction, especially in enterprise and large-scale projects. Many major frameworks (Angular, NestJS) are built with TypeScript, and React/Vue projects widely adopt it.
        *   **`@types` Packages:** A vast repository of type definitions for almost every popular JavaScript library and framework, allowing TypeScript projects to leverage existing JS libraries with type safety.
        *   **Growing Library Support:** Many new libraries are being written directly in TypeScript or include type definitions out-of-the-box.
    *   **Cons:**
        *   **Dependency on Type Definitions:** Occasionally, a niche JavaScript library might not have up-to-date or accurate type definitions, requiring manual creation or maintenance of declaration files.
    
    **JavaScript**
    *   **Pros:**
        *   **Ubiquitous and Mature:** The largest and most mature ecosystem in web development. Every browser, Node.js, and countless tools support JavaScript natively.
        *   **Vast Number of Libraries and Frameworks:** An unparalleled number of open-source libraries, frameworks, and tools are available, covering almost every conceivable use case.
        *   **Direct Compatibility:** Any JavaScript code can run directly without modification.
    *   **Cons:**
        *   **Runtime Type Issues:** While the ecosystem is vast, the lack of static types means that integrating different libraries can sometimes lead to runtime type mismatches if not carefully managed.
    
    ### Conclusion
    
    For a **large-scale application**, **TypeScript generally offers significant advantages over JavaScript**. The benefits of static type checking in terms of reduced bugs, improved code quality, safer refactoring, and superior tooling far outweigh the initial learning curve and build overhead. In a large team working on a complex codebase, TypeScript's ability to enforce contracts and provide clear documentation through types becomes invaluable for maintainability, collaboration, and long-term stability. While JavaScript remains highly flexible and has a massive ecosystem, its dynamic nature makes it inherently riskier and more challenging to manage effectively at scale without extensive testing and rigorous development practices to compensate for the lack of type safety.
  00:00:52.516 INFO  ◉ [think]      1 steps | 1,739 tok | 0.0s
  00:00:52.538 INFO  Execution completed {"taskId":"01KNN68AEG77VWHGQ5SFSMJ9NC","success":true,"tokensUsed":1739,"cost":0.0009502499999999999,"duration":10086}
  00:00:52.538 INFO  ◉ [complete]   ✓ 01KNN68AEG77VWHGQ5SFSMJ9NC | 1,739 tok | $0.0010 | 10.1s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:00:42.452 INFO  Execution started {"taskId":"01KNN68AEG77VWHGQ5SFSMJ9NC","agentId":"test-react--multi-step-analysis-1775606442395"}
  00:00:42.461 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 9ms
  00:00:42.462 INFO  ◉ [strategy]   reactive
  00:00:52.512 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:00 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Compare the pros and cons of using TypeScript vs JavaScript for a large-scale application. Consider at least: type safety, tooling, learning curve, and ecosystem. Provide a structured comparison.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Compare the pros and cons of using TypeScript vs JavaScript for a large-scale application. Consider at least: type safety, tooling, learning curve, and ecosystem. Provide a structured comparison.
    ── raw response ──
    When comparing TypeScript and JavaScript for a large-scale application, several key factors come into play. Here's a structured comparison focusing on type safety, tooling, learning curve, and ecosystem:
    
    ## TypeScript vs. JavaScript for Large-Scale Applications
    
    ### 1. Type Safety
    
    **TypeScript**
    *   **Pros:**
        *   **Enhanced Type Safety:** Provides static type checking at compile time, catching many common errors (e.g., typos, incorrect function arguments, null/undefined issues) before runtime. This significantly reduces bugs in large codebases.
        *   **Improved Code Quality:** Enforces a contract for data structures and function signatures, leading to more predictable and robust code.
        *   **Better Refactoring:** The type system makes refactoring large codebases much safer, as the compiler can highlight breaking changes.
    *   **Cons:**
        *   **Initial Overhead:** Requires developers to explicitly define types, which can feel like extra work initially.
        *   **Complexity for Advanced Types:** While powerful, advanced type definitions can sometimes be complex and require a deeper understanding of TypeScript's type system.
    
    **JavaScript**
    *   **Pros:**
        *   **Flexibility:** Its dynamic nature allows for rapid prototyping and less rigid code structure, which can be beneficial in very early stages or for smaller projects.
    *   **Cons:**
        *   **Lack of Type Safety:** Being dynamically typed, JavaScript defers type checking to runtime. This means type-related errors often manifest as runtime bugs, which are harder to debug and more costly to fix in large applications.
        *   **Reduced Predictability:** Without explicit types, it can be challenging to understand the expected data types for variables and function parameters, especially when working with unfamiliar code or in large teams.
        *   **Difficult Refactoring:** Refactoring large JavaScript codebases can be risky, as there's no compiler to catch type-related errors introduced by changes.
    
    ### 2. Tooling
    
    **TypeScript**
    *   **Pros:**
        *   **Superior IDE Support:** Due to its type information, TypeScript enables powerful features in IDEs like Visual Studio Code, WebStorm, etc. This includes:
            *   **IntelliSense/Autocompletion:** Highly accurate and context-aware suggestions.
            *   **Go-to-Definition:** Easily navigate to the source of a variable, function, or type.
            *   **Refactoring Tools:** Safer and more reliable automated refactoring.
            *   **Error Highlighting:** Real-time feedback on type errors.
        *   **Enhanced Debugging:** Type information can aid in understanding variable states during debugging.
        *   **Code Generation/Documentation:** Types can be used to generate documentation or even API clients.
    *   **Cons:**
        *   **Build Step Required:** TypeScript code needs to be transpiled into JavaScript before it can be run in browsers or Node.js environments, adding an extra step to the development workflow (though this is common with modern JavaScript anyway).
    
    **JavaScript**
    *   **Pros:**
        *   **Direct Execution:** No transpilation step is strictly required for basic JavaScript, allowing for immediate execution.
        *   **Mature Ecosystem of Linters/Formatters:** Tools like ESLint and Prettier are highly mature and widely adopted for maintaining code quality and consistency.
    *   **Cons:**
        *   **Limited IDE Support for Type-Related Features:** While IDEs provide good general JavaScript support, they cannot offer the same level of type-aware IntelliSense, refactoring, or error checking as with TypeScript.
        *   **Reliance on External Tools for Type Inference:** Tools like JSDoc comments or external type inference libraries are often used to compensate for the lack of native type information, adding complexity.
    
    ### 3. Learning Curve
    
    **TypeScript**
    *   **Pros:**
        *   **Gradual Adoption:** Can be introduced incrementally into existing JavaScript projects.
        *   **Familiar Syntax:** As a superset of JavaScript, developers familiar with JS will find most of the syntax familiar.
    *   **Cons:**
        *   **Initial Learning Overhead:** Developers new to static typing will need to learn TypeScript's type syntax, concepts (interfaces, types, generics, enums), and how to configure the compiler.
        *   **Increased Cognitive Load:** For some, managing types alongside business logic can initially feel like additional mental overhead.
        *   **Debugging Type Errors:** Understanding and resolving complex type errors can be challenging for beginners.
    
    **JavaScript**
    *   **Pros:**
        *   **Lower Barrier to Entry:** Developers can start writing code quickly without needing to learn any additional type syntax or configuration.
        *   **Flexibility:** Its dynamic nature can feel less restrictive for beginners.
    *   **Cons:**
        *   **Hidden Complexity:** While easy to start, managing complexity and avoiding runtime errors in large JavaScript projects requires significant discipline, extensive testing, and often a deep understanding of potential type pitfalls.
        *   **Harder to Onboard New Developers:** Without types, new team members might take longer to understand the expected data structures and function contracts within a large codebase.
    
    ### 4. Ecosystem
    
    **TypeScript**
    *   **Pros:**
        *   **Strong Community Adoption:** Gaining massive traction, especially in enterprise and large-scale projects. Many major frameworks (Angular, NestJS) are built with TypeScript, and React/Vue projects widely adopt it.
        *   **`@types` Packages:** A vast repository of type definitions for almost every popular JavaScript library and framework, allowing TypeScript projects to leverage existing JS libraries with type safety.
        *   **Growing Library Support:** Many new libraries are being written directly in TypeScript or include type definitions out-of-the-box.
    *   **Cons:**
        *   **Dependency on Type Definitions:** Occasionally, a niche JavaScript library might not have up-to-date or accurate type definitions, requiring manual creation or maintenance of declaration files.
    
    **JavaScript**
    *   **Pros:**
        *   **Ubiquitous and Mature:** The largest and most mature ecosystem in web development. Every browser, Node.js, and countless tools support JavaScript natively.
        *   **Vast Number of Libraries and Frameworks:** An unparalleled number of open-source libraries, frameworks, and tools are available, covering almost every conceivable use case.
        *   **Direct Compatibility:** Any JavaScript code can run directly without modification.
    *   **Cons:**
        *   **Runtime Type Issues:** While the ecosystem is vast, the lack of static types means that integrating different libraries can sometimes lead to runtime type mismatches if not carefully managed.
    
    ### Conclusion
    
    For a **large-scale application**, **TypeScript generally offers significant advantages over JavaScript**. The benefits of static type checking in terms of reduced bugs, improved code quality, safer refactoring, and superior tooling far outweigh the initial learning curve and build overhead. In a large team working on a complex codebase, TypeScript's ability to enforce contracts and provide clear documentation through types becomes invaluable for maintainability, collaboration, and long-term stability. While JavaScript remains highly flexible and has a massive ecosystem, its dynamic nature makes it inherently riskier and more challenging to manage effectively at scale without extensive testing and rigorous development practices to compensate for the lack of type safety.
  00:00:52.516 INFO  ◉ [think]      1 steps | 1,739 tok | 0.0s
  00:00:52.538 INFO  Execution completed {"taskId":"01KNN68AEG77VWHGQ5SFSMJ9NC","success":true,"tokensUsed":1739,"cost":0.0009502499999999999,"duration":10086}
  00:00:52.538 INFO  ◉ [complete]   ✓ 01KNN68AEG77VWHGQ5SFSMJ9NC | 1,739 tok | $0.0010 | 10.1s

═══ Spans (9) ═══
  ✓ execution.run (10092.5ms) [46d24cb6…]
    ✓ execution.phase.bootstrap (7.9ms) [46d24cb6…]
      ✓ phase.bootstrap.metrics (0.0ms) [46d24cb6…]
    ✓ execution.phase.strategy-select (0.9ms) [46d24cb6…]
      ✓ phase.strategy-select.metrics (0.0ms) [46d24cb6…]
    ✓ execution.phase.think (10053.9ms) [46d24cb6…]
      ✓ phase.think.metrics (0.0ms) [46d24cb6…]
    ✓ execution.phase.complete (2.4ms) [46d24cb6…]
      ✓ phase.complete.metrics (0.0ms) [46d24cb6…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 10.1s   Steps: 1        │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 1,739 │
│ Cost:     ~$0.003                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            7ms
├─ ✅  [strategy-select]      1ms
├─ ⚠️  [think]              10.1s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →

⚠️  Alerts & Insights
└─ ⚠️  think phase blocked ≥10s (LLM latency)
✓ 10.1s (1 iters, 1739 tok)
  ⊙ [reasoning   ] Plan-Execute: structured task                 ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:00:52.618 INFO  Execution started {"taskId":"01KNN68MC652XPQBPCW1EZYAX3","agentId":"test-plan-execute--structured-task-1775606452559"}
  00:00:52.627 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 9ms
  00:00:52.628 INFO  ◉ [strategy]   plan-execute-reflect
  00:00:54.657 DEBUG   ┄ [model-io:structured-output]
    ── system ──
    You are a planning agent. Decompose the goal into structured steps.
    
    Respond with ONLY valid JSON. No markdown, no explanation, no thinking tags.
    ── user ──
    You are a planning agent. Decompose the goal into the MINIMUM number of steps needed.
    
    PLANNING RULES:
    - Use the FEWEST steps possible. Combine related work into one step.
    - Prefer "tool_call" steps — they execute instantly without LLM overhead.
    - Use at most ONE "analysis" step to do all reasoning/writing/composition work.
    - Use {{from_step:sN}} in toolArgs to pass previous step results to tool calls.
    - Never split summarizing, formatting, and composing into separate steps — combine them.
    
    GOAL:
    Design a REST API for a simple todo application. Include: resource paths, HTTP methods, request/response formats, and error handling. Return the design as a structured specification.
    
    AVAILABLE TOOLS:
    None — use "analysis" type steps only.
    
    OUTPUT FORMAT:
    Respond with a JSON object containing a "steps" array. Each step has this schema:
    {
      "title": "string — short name for this step",
      "instruction": "string — what the LLM or tool should do",
      "type": "tool_call" | "analysis" | "composite",
      "toolName": "string (optional) — tool to call if type is tool_call",
      "toolArgs": "object (optional) — ALL required arguments for the tool. Use {{from_step:sN}} to inject the result of a previous step as a string value",
      "toolHints": ["string"] (optional) — tool names available for composite steps",
      "dependsOn": ["string"] (optional) — step IDs that must complete first"
    }
    
    Step types:
    - "tool_call": calls a specific tool (set toolName and toolArgs with ALL required params)
    - "analysis": LLM reasoning/writing (no tool needed)
    - "composite": multi-tool sub-task (set toolHints for available tools)
    
    IMPORTANT for tool_call steps:
    - Include ALL required parameters in toolArgs
    - To use output from a PREVIOUS step as an argument value, use {{from_step:sN}} where N is an EARLIER step number
    - A step can ONLY reference steps that come BEFORE it (e.g., s3 can reference s1 or s2, NOT s3 itself)
    - Example: s3 with {"message": "{{from_step:s2}}"} passes s2's result as the "message" argument
    
    EXAMPLE:
    {
      "steps": [
        {
          "title": "Fetch recent commits",
          "instruction": "Get the last 10 commits from the main branch",
          "type": "tool_call",
          "toolName": "github/list_commits",
          "toolArgs": { "owner": "acme", "repo": "app", "perPage": 10 }
        },
        {
          "title": "Summarize changes",
          "instruction": "Analyze the commits and write a brief summary",
          "type": "analysis",
          "dependsOn": ["s1"]
        },
        {
          "title": "Send summary to user",
          "instruction": "Send the commit summary via messaging",
          "type": "tool_call",
          "toolName": "messaging/send",
          "toolArgs": { "recipient": "user@example.com", "message": "{{from_step:s2}}" },
          "dependsOn": ["s2"]
        }
      ]
    }
    
    JSON only, no explanation:
    
    Respond with ONLY a JSON object matching the schema above. No markdown fences, no explanation.
  00:00:54.671 DEBUG   ┄ [thought]  [PLAN 1] Generated 1 steps:
  s1: Design REST API for Todo Application (analysis)
  00:00:54.672 DEBUG   ┄ [thought]  [SCHEDULE] 1 steps sequential
  00:00:54.673 DEBUG   ┄ [action]   [STEP 1/1] s1: Design REST API for Todo Application (analysis)
  00:01:12.079 DEBUG   ┄ [obs]      [EXEC s1] ✓ **REST API Specification: Simple Todo Application**

**Base URL:** `/api/v1`

---

**I. Resources**

*   **Task:** Represents a single todo item.

---

**II. Data Models**

**A. Task Object**
Represents the full state of a task.

```json
{
  "id": "string",         // Unique identifier for the task (e.g., UUID, generated by server)
  "title": "string",      // The task's title/description (required)
  "description": "string",// Optional, more detailed description
  "completed": "boolean", // True if the task is completed, false otherwise (default: false)
  "createdAt": "string",  // ISO 8601 timestamp of creation (e.g., "2023-10-26T10:00:00Z")
  "updatedAt": "string"   // ISO 8601 timestamp of last update
}
```

**B. New Task Request Object**
Used for creating a new task.

```json
{
  "title": "string",      // Required. Max length 255 characters.
  "description": "string" // Optional. Max length 1024 characters.
}
```

**C. Update Task Request Object (for PUT)**
Used for fully replacing an existing task. All fields are required to be present in the request body, except `id`, `createdAt`, `updatedAt`.

```json
{
  "title": "string",      // Required. Max length 255 characters.
  "description": "string",// Optional. Max length 1024 characters. Can be null.
  "completed": "boolean"  // Required.
}
```

---

**III. API Endpoints**

**A. Get All Tasks**

*   **Path:** `/tasks`
*   **Method:** `GET`
*   **Description:** Retrieves a list of all tasks.
*   **Request Body:** None
*   **Response Body (Success - `200 OK`):** An array of Task Objects.
    ```json
    [
      {
        "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
        "title": "Buy groceries",
        "description": "Milk, eggs, bread",
        "completed": false,
        "createdAt": "2023-10-26T10:00:00Z",
        "updatedAt": "2023-10-26T10:00:00Z"
      },
      {
        "id": "b2c3d4e5-f6a7-8901-2345-67890abcdef0",
        "title": "Walk the dog",
        "description": null,
        "completed": true,
        "createdAt": "2023-10-25T09:00:00Z",
        "updatedAt": "2023-10-25T11:30:00Z"
      }
    ]
    ```
*   **Error Responses:** See Section IV.

**B. Create a New Task**

*   **Path:** `/tasks`
*   **Method:** `POST`
*   **Description:** Creates a new task. The server assigns an `id` and sets `createdAt`, `updatedAt`, and `completed` (to `false`).
*   **Request Body:** New Task Request Object.
    ```json
    {
      "title": "Learn REST API design",
      "description": "Read documentation and practice"
    }
    ```
*   **Response Body (Success - `201 Created`):** The created Task Object.
    ```json
    {
      "id": "c3d4e5f6-a7b8-9012-3456-7890abcdef01",
      "title": "Learn REST API design",
      "description": "Read documentation and practice",
      "completed": false,
      "createdAt": "2023-10-26T14:30:00Z",
      "updatedAt": "2023-10-26T14:30:00Z"
    }
    ```
*   **Error Responses:**
    *   `400 Bad Request`: If `title` is missing, empty, or exceeds max length. (See Section IV)

**C. Get a Single Task**

*   **Path:** `/tasks/{id}`
*   **Method:** `GET`
*   **Description:** Retrieves a single task by its ID.
*   **Request Body:** None
*   **Response Body (Success - `200 OK`):** A Task Object.
    ```json
    {
      "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
      "title": "Buy groceries",
      "description": "Milk, eggs, bread",
      "completed": false,
      "createdAt": "2023-10-26T10:00:00Z",
      "updatedAt": "2023-10-26T10:00:00Z"
    }
    ```
*   **Error Responses:**
    *   `404 Not Found`: If the task with the given ID does not exist. (See Section IV)

**D. Update an Existing Task**

*   **Path:** `/tasks/{id}`
*   **Method:** `PUT`
*   **Description:** Updates an existing task identified by its ID. This is a full replacement; the provided `Update Task Request Object` will completely overwrite the existing task's `title`, `description`, and `completed` status. `createdAt` remains unchanged, `updatedAt` is updated by the server.
*   **Request Body:** Update Task Request Object.
    ```json
    {
      "title": "Buy organic groceries",
      "description": "Milk, eggs, bread, organic vegetables",
      "completed": true
    }
    ```
*   **Response Body (Success - `200 OK`):** The updated Task Object.
    ```json
    {
      "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  00:01:16.801 DEBUG   ┄ [thought]  [REFLECT 1] ✓ SATISFIED UNSATISFIED: The design is incomplete; error handling details are missing, and the DELETE endpoint for tasks is not included.

The `STEP RESULTS` explicitly refers to "Section IV. Error Handling" multiple times, but this section is entirely absent from the provided output. Additionally, a fundamental CRUD operation, the `DELETE /tasks/{id}` endpoint, is missing from the API design, which is essential for a simple todo application. The example response body for the `PUT /tasks/{id}` endpoint is also cut off.
  00:01:27.166 INFO  ◉ [think]      4 steps | 8,549 tok | 0.0s
  00:01:27.759 INFO  Execution completed {"taskId":"01KNN68MC652XPQBPCW1EZYAX3","success":true,"tokensUsed":8549,"cost":0.0030726,"duration":35141}
  00:01:27.759 INFO  ◉ [complete]   ✓ 01KNN68MC652XPQBPCW1EZYAX3 | 8,549 tok | $0.0031 | 35.1s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (12) ═══
  00:00:52.618 INFO  Execution started {"taskId":"01KNN68MC652XPQBPCW1EZYAX3","agentId":"test-plan-execute--structured-task-1775606452559"}
  00:00:52.627 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 9ms
  00:00:52.628 INFO  ◉ [strategy]   plan-execute-reflect
  00:00:54.657 DEBUG   ┄ [model-io:structured-output]
    ── system ──
    You are a planning agent. Decompose the goal into structured steps.
    
    Respond with ONLY valid JSON. No markdown, no explanation, no thinking tags.
    ── user ──
    You are a planning agent. Decompose the goal into the MINIMUM number of steps needed.
    
    PLANNING RULES:
    - Use the FEWEST steps possible. Combine related work into one step.
    - Prefer "tool_call" steps — they execute instantly without LLM overhead.
    - Use at most ONE "analysis" step to do all reasoning/writing/composition work.
    - Use {{from_step:sN}} in toolArgs to pass previous step results to tool calls.
    - Never split summarizing, formatting, and composing into separate steps — combine them.
    
    GOAL:
    Design a REST API for a simple todo application. Include: resource paths, HTTP methods, request/response formats, and error handling. Return the design as a structured specification.
    
    AVAILABLE TOOLS:
    None — use "analysis" type steps only.
    
    OUTPUT FORMAT:
    Respond with a JSON object containing a "steps" array. Each step has this schema:
    {
      "title": "string — short name for this step",
      "instruction": "string — what the LLM or tool should do",
      "type": "tool_call" | "analysis" | "composite",
      "toolName": "string (optional) — tool to call if type is tool_call",
      "toolArgs": "object (optional) — ALL required arguments for the tool. Use {{from_step:sN}} to inject the result of a previous step as a string value",
      "toolHints": ["string"] (optional) — tool names available for composite steps",
      "dependsOn": ["string"] (optional) — step IDs that must complete first"
    }
    
    Step types:
    - "tool_call": calls a specific tool (set toolName and toolArgs with ALL required params)
    - "analysis": LLM reasoning/writing (no tool needed)
    - "composite": multi-tool sub-task (set toolHints for available tools)
    
    IMPORTANT for tool_call steps:
    - Include ALL required parameters in toolArgs
    - To use output from a PREVIOUS step as an argument value, use {{from_step:sN}} where N is an EARLIER step number
    - A step can ONLY reference steps that come BEFORE it (e.g., s3 can reference s1 or s2, NOT s3 itself)
    - Example: s3 with {"message": "{{from_step:s2}}"} passes s2's result as the "message" argument
    
    EXAMPLE:
    {
      "steps": [
        {
          "title": "Fetch recent commits",
          "instruction": "Get the last 10 commits from the main branch",
          "type": "tool_call",
          "toolName": "github/list_commits",
          "toolArgs": { "owner": "acme", "repo": "app", "perPage": 10 }
        },
        {
          "title": "Summarize changes",
          "instruction": "Analyze the commits and write a brief summary",
          "type": "analysis",
          "dependsOn": ["s1"]
        },
        {
          "title": "Send summary to user",
          "instruction": "Send the commit summary via messaging",
          "type": "tool_call",
          "toolName": "messaging/send",
          "toolArgs": { "recipient": "user@example.com", "message": "{{from_step:s2}}" },
          "dependsOn": ["s2"]
        }
      ]
    }
    
    JSON only, no explanation:
    
    Respond with ONLY a JSON object matching the schema above. No markdown fences, no explanation.
  00:00:54.671 DEBUG   ┄ [thought]  [PLAN 1] Generated 1 steps:
  s1: Design REST API for Todo Application (analysis)
  00:00:54.672 DEBUG   ┄ [thought]  [SCHEDULE] 1 steps sequential
  00:00:54.673 DEBUG   ┄ [action]   [STEP 1/1] s1: Design REST API for Todo Application (analysis)
  00:01:12.079 DEBUG   ┄ [obs]      [EXEC s1] ✓ **REST API Specification: Simple Todo Application**

**Base URL:** `/api/v1`

---

**I. Resources**

*   **Task:** Represents a single todo item.

---

**II. Data Models**

**A. Task Object**
Represents the full state of a task.

```json
{
  "id": "string",         // Unique identifier for the task (e.g., UUID, generated by server)
  "title": "string",      // The task's title/description (required)
  "description": "string",// Optional, more detailed description
  "completed": "boolean", // True if the task is completed, false otherwise (default: false)
  "createdAt": "string",  // ISO 8601 timestamp of creation (e.g., "2023-10-26T10:00:00Z")
  "updatedAt": "string"   // ISO 8601 timestamp of last update
}
```

**B. New Task Request Object**
Used for creating a new task.

```json
{
  "title": "string",      // Required. Max length 255 characters.
  "description": "string" // Optional. Max length 1024 characters.
}
```

**C. Update Task Request Object (for PUT)**
Used for fully replacing an existing task. All fields are required to be present in the request body, except `id`, `createdAt`, `updatedAt`.

```json
{
  "title": "string",      // Required. Max length 255 characters.
  "description": "string",// Optional. Max length 1024 characters. Can be null.
  "completed": "boolean"  // Required.
}
```

---

**III. API Endpoints**

**A. Get All Tasks**

*   **Path:** `/tasks`
*   **Method:** `GET`
*   **Description:** Retrieves a list of all tasks.
*   **Request Body:** None
*   **Response Body (Success - `200 OK`):** An array of Task Objects.
    ```json
    [
      {
        "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
        "title": "Buy groceries",
        "description": "Milk, eggs, bread",
        "completed": false,
        "createdAt": "2023-10-26T10:00:00Z",
        "updatedAt": "2023-10-26T10:00:00Z"
      },
      {
        "id": "b2c3d4e5-f6a7-8901-2345-67890abcdef0",
        "title": "Walk the dog",
        "description": null,
        "completed": true,
        "createdAt": "2023-10-25T09:00:00Z",
        "updatedAt": "2023-10-25T11:30:00Z"
      }
    ]
    ```
*   **Error Responses:** See Section IV.

**B. Create a New Task**

*   **Path:** `/tasks`
*   **Method:** `POST`
*   **Description:** Creates a new task. The server assigns an `id` and sets `createdAt`, `updatedAt`, and `completed` (to `false`).
*   **Request Body:** New Task Request Object.
    ```json
    {
      "title": "Learn REST API design",
      "description": "Read documentation and practice"
    }
    ```
*   **Response Body (Success - `201 Created`):** The created Task Object.
    ```json
    {
      "id": "c3d4e5f6-a7b8-9012-3456-7890abcdef01",
      "title": "Learn REST API design",
      "description": "Read documentation and practice",
      "completed": false,
      "createdAt": "2023-10-26T14:30:00Z",
      "updatedAt": "2023-10-26T14:30:00Z"
    }
    ```
*   **Error Responses:**
    *   `400 Bad Request`: If `title` is missing, empty, or exceeds max length. (See Section IV)

**C. Get a Single Task**

*   **Path:** `/tasks/{id}`
*   **Method:** `GET`
*   **Description:** Retrieves a single task by its ID.
*   **Request Body:** None
*   **Response Body (Success - `200 OK`):** A Task Object.
    ```json
    {
      "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
      "title": "Buy groceries",
      "description": "Milk, eggs, bread",
      "completed": false,
      "createdAt": "2023-10-26T10:00:00Z",
      "updatedAt": "2023-10-26T10:00:00Z"
    }
    ```
*   **Error Responses:**
    *   `404 Not Found`: If the task with the given ID does not exist. (See Section IV)

**D. Update an Existing Task**

*   **Path:** `/tasks/{id}`
*   **Method:** `PUT`
*   **Description:** Updates an existing task identified by its ID. This is a full replacement; the provided `Update Task Request Object` will completely overwrite the existing task's `title`, `description`, and `completed` status. `createdAt` remains unchanged, `updatedAt` is updated by the server.
*   **Request Body:** Update Task Request Object.
    ```json
    {
      "title": "Buy organic groceries",
      "description": "Milk, eggs, bread, organic vegetables",
      "completed": true
    }
    ```
*   **Response Body (Success - `200 OK`):** The updated Task Object.
    ```json
    {
      "id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  00:01:16.801 DEBUG   ┄ [thought]  [REFLECT 1] ✓ SATISFIED UNSATISFIED: The design is incomplete; error handling details are missing, and the DELETE endpoint for tasks is not included.

The `STEP RESULTS` explicitly refers to "Section IV. Error Handling" multiple times, but this section is entirely absent from the provided output. Additionally, a fundamental CRUD operation, the `DELETE /tasks/{id}` endpoint, is missing from the API design, which is essential for a simple todo application. The example response body for the `PUT /tasks/{id}` endpoint is also cut off.
  00:01:27.166 INFO  ◉ [think]      4 steps | 8,549 tok | 0.0s
  00:01:27.759 INFO  Execution completed {"taskId":"01KNN68MC652XPQBPCW1EZYAX3","success":true,"tokensUsed":8549,"cost":0.0030726,"duration":35141}
  00:01:27.759 INFO  ◉ [complete]   ✓ 01KNN68MC652XPQBPCW1EZYAX3 | 8,549 tok | $0.0031 | 35.1s

═══ Spans (11) ═══
  ✓ execution.run (35142.5ms) [2a45a626…]
    ✓ execution.phase.bootstrap (7.5ms) [2a45a626…]
      ✓ phase.bootstrap.metrics (0.0ms) [2a45a626…]
    ✓ execution.phase.strategy-select (0.8ms) [2a45a626…]
      ✓ phase.strategy-select.metrics (0.0ms) [2a45a626…]
    ✓ execution.phase.think (34524.3ms) [2a45a626…]
      ✓ phase.think.metrics (0.1ms) [2a45a626…]
    ✓ execution.phase.memory-flush (588.7ms) [2a45a626…]
      ✓ phase.memory-flush.metrics (0.0ms) [2a45a626…]
    ✓ execution.phase.complete (1.0ms) [2a45a626…]
      ✓ phase.complete.metrics (0.0ms) [2a45a626…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 35.1s   Steps: 4        │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 8,549 │
│ Cost:     ~$0.013                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            7ms
├─ ✅  [strategy-select]      1ms
├─ ⚠️  [think]              34.5s (4 iter, 98% of time)
├─ ✅  [memory-flush]       589ms
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: B   Signal: flat   Mean: 0.366   Delta: +0.433
├─ Model stalled — entropy didn't decrease across iterations
├─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
└─  iter  3 ████████████░░░░░░░░ 0.583 →
   ┈┈┈
└─ 💡 Consider enabling strategy switching (.withReasoning({ enableStrategySwitching: true }))

⚠️  Alerts & Insights
└─ ⚠️  think phase blocked ≥10s (LLM latency)
✓ 35.2s (4 iters, 8549 tok)
  ⊙ [reasoning   ] Adaptive: let framework choose                ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:01:27.843 INFO  Execution started {"taskId":"01KNN69PRPV71BXP1DSRYK5P5T","agentId":"test-adaptive--let-framework-choose-1775606487771"}
  00:01:27.860 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 25ms
  00:01:27.862 INFO  ◉ [strategy]   adaptive
  00:01:27.864 DEBUG   ┄ [thought]  [ADAPTIVE] Heuristic: reactive
  00:01:33.367 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain the difference between concurrency and parallelism with a real-world analogy.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain the difference between concurrency and parallelism with a real-world analogy.
    ── raw response ──
    Concurrency and parallelism are related concepts in computing, but they describe different aspects of handling multiple tasks:
    
    *   **Concurrency** is about *managing* multiple tasks at the same time. It deals with the structure of a program to handle multiple independent tasks, often by rapidly switching between them on a single processing unit. It gives the *illusion* that multiple tasks are progressing simultaneously, even if only one physical operation is happening at any given instant. The focus is on dealing with many things at once.
    
    *   **Parallelism** is about *executing* multiple tasks at the exact same time. It requires multiple processing units (like CPU cores) to physically perform different tasks simultaneously. The focus is on doing many things at once.
    
    Here's a real-world analogy to illustrate the difference:
    
    **Analogy: A Coffee Shop**
    
    Imagine a busy coffee shop receiving multiple customer orders.
    
    *   **Concurrency (Single Barista):**
        *   You have **one barista** working alone.
        *   A customer orders a latte, another orders a cappuccino, and a third orders an espresso.
        *   The barista starts brewing the espresso. While it's brewing, they start steaming milk for the latte. Then, they pour the espresso, add the steamed milk to the latte, and finally prepare the cappuccino.
        *   The barista is **concurrently** handling all three orders. They are making progress on each order by switching between tasks, giving the impression that all orders are being worked on simultaneously, even though only one physical action (brewing, steaming, pouring) is happening at any given moment. They are *managing* multiple tasks.
    
    *   **Parallelism (Multiple Baristas):**
        *   Now, imagine there are **three baristas** working simultaneously in the coffee shop.
        *   Barista 1 is solely making the latte.
        *   Barista 2 is solely making the cappuccino.
        *   Barista 3 is solely making the espresso.
        *   All three orders are being prepared **at the exact same time** by different baristas. This is **parallelism**. They are *executing* multiple tasks simultaneously.
    
    In summary:
    *   **Concurrency** is like the single barista juggling multiple orders, making progress on all of them over time.
    *   **Parallelism** is like having multiple baristas, each working on a different order simultaneously, completing them much faster.
  00:01:33.370 INFO  ◉ [think]      2 steps | 671 tok | 0.0s
  00:01:33.383 INFO  Execution completed {"taskId":"01KNN69PRPV71BXP1DSRYK5P5T","success":true,"tokensUsed":671,"cost":0.00033015000000000003,"duration":5548}
  00:01:33.383 INFO  ◉ [complete]   ✓ 01KNN69PRPV71BXP1DSRYK5P5T | 671 tok | $0.0003 | 5.5s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (8) ═══
  00:01:27.843 INFO  Execution started {"taskId":"01KNN69PRPV71BXP1DSRYK5P5T","agentId":"test-adaptive--let-framework-choose-1775606487771"}
  00:01:27.860 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 25ms
  00:01:27.862 INFO  ◉ [strategy]   adaptive
  00:01:27.864 DEBUG   ┄ [thought]  [ADAPTIVE] Heuristic: reactive
  00:01:33.367 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain the difference between concurrency and parallelism with a real-world analogy.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain the difference between concurrency and parallelism with a real-world analogy.
    ── raw response ──
    Concurrency and parallelism are related concepts in computing, but they describe different aspects of handling multiple tasks:
    
    *   **Concurrency** is about *managing* multiple tasks at the same time. It deals with the structure of a program to handle multiple independent tasks, often by rapidly switching between them on a single processing unit. It gives the *illusion* that multiple tasks are progressing simultaneously, even if only one physical operation is happening at any given instant. The focus is on dealing with many things at once.
    
    *   **Parallelism** is about *executing* multiple tasks at the exact same time. It requires multiple processing units (like CPU cores) to physically perform different tasks simultaneously. The focus is on doing many things at once.
    
    Here's a real-world analogy to illustrate the difference:
    
    **Analogy: A Coffee Shop**
    
    Imagine a busy coffee shop receiving multiple customer orders.
    
    *   **Concurrency (Single Barista):**
        *   You have **one barista** working alone.
        *   A customer orders a latte, another orders a cappuccino, and a third orders an espresso.
        *   The barista starts brewing the espresso. While it's brewing, they start steaming milk for the latte. Then, they pour the espresso, add the steamed milk to the latte, and finally prepare the cappuccino.
        *   The barista is **concurrently** handling all three orders. They are making progress on each order by switching between tasks, giving the impression that all orders are being worked on simultaneously, even though only one physical action (brewing, steaming, pouring) is happening at any given moment. They are *managing* multiple tasks.
    
    *   **Parallelism (Multiple Baristas):**
        *   Now, imagine there are **three baristas** working simultaneously in the coffee shop.
        *   Barista 1 is solely making the latte.
        *   Barista 2 is solely making the cappuccino.
        *   Barista 3 is solely making the espresso.
        *   All three orders are being prepared **at the exact same time** by different baristas. This is **parallelism**. They are *executing* multiple tasks simultaneously.
    
    In summary:
    *   **Concurrency** is like the single barista juggling multiple orders, making progress on all of them over time.
    *   **Parallelism** is like having multiple baristas, each working on a different order simultaneously, completing them much faster.
  00:01:33.370 INFO  ◉ [think]      2 steps | 671 tok | 0.0s
  00:01:33.383 INFO  Execution completed {"taskId":"01KNN69PRPV71BXP1DSRYK5P5T","success":true,"tokensUsed":671,"cost":0.00033015000000000003,"duration":5548}
  00:01:33.383 INFO  ◉ [complete]   ✓ 01KNN69PRPV71BXP1DSRYK5P5T | 671 tok | $0.0003 | 5.5s

═══ Spans (9) ═══
  ✓ execution.run (5548.8ms) [a9c4ba47…]
    ✓ execution.phase.bootstrap (16.9ms) [a9c4ba47…]
      ✓ phase.bootstrap.metrics (0.0ms) [a9c4ba47…]
    ✓ execution.phase.strategy-select (0.9ms) [a9c4ba47…]
      ✓ phase.strategy-select.metrics (0.0ms) [a9c4ba47…]
    ✓ execution.phase.think (5507.8ms) [a9c4ba47…]
      ✓ phase.think.metrics (0.0ms) [a9c4ba47…]
    ✓ execution.phase.complete (2.1ms) [a9c4ba47…]
  ✓ phase.complete.metrics (0.0ms) [a9c4ba47…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 5.5s   Steps: 2       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 671 │
│ Cost:     ~$0.001                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           17ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               5.5s (2 iter, 100% of time)
└─ ✅  [complete]             2ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 5.6s (2 iters, 671 tok)
  ⊙ [tools       ] Recall tool usage                             ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:01:33.456 INFO  Execution started {"taskId":"01KNN69W8CS2FT0WJ4H593NDAH","agentId":"test-recall-tool-usage-1775606493388"}
  00:01:33.459 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:01:33.460 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute
  00:01:37.338 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
  00:01:37.341 DEBUG   ┄ [action]   {"tool":"recall","input":"{\"key\":\"answer\",\"content\":\"The capital of France is Paris\"}"}
  00:01:37.342 DEBUG   ┄ [obs]      [Tool error: Tool "recall" not found]
  00:01:40.111 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
    
    Error: [Tool error: Tool "recall" not found] — skip this, use data from other calls
    Produce the output now.
    ── raw response ──
    I am unable to use the `recall` tool as it is not found in my available tools, as indicated by the error: "Tool error: Tool "recall" not found". Therefore, I cannot store or retrieve the note as requested.
  00:01:40.116 INFO  ◉ [think]      4 steps | 5,254 tok | 0.0s
  00:01:40.134 INFO  ◉ [act]        recall (1 tools)
  00:01:45.073 INFO  Execution completed {"taskId":"01KNN69W8CS2FT0WJ4H593NDAH","success":true,"tokensUsed":5254,"cost":0.0008205,"duration":11618}
  00:01:45.073 INFO  ◉ [complete]   ✓ 01KNN69W8CS2FT0WJ4H593NDAH | 5,254 tok | $0.0008 | 11.6s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (11) ═══
  00:01:33.456 INFO  Execution started {"taskId":"01KNN69W8CS2FT0WJ4H593NDAH","agentId":"test-recall-tool-usage-1775606493388"}
  00:01:33.459 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:01:33.460 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute
  00:01:37.338 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
  00:01:37.341 DEBUG   ┄ [action]   {"tool":"recall","input":"{\"key\":\"answer\",\"content\":\"The capital of France is Paris\"}"}
  00:01:37.342 DEBUG   ┄ [obs]      [Tool error: Tool "recall" not found]
  00:01:40.111 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Use the recall tool to store a note with key 'answer' containing 'The capital of France is Paris', then retrieve it and include the EXACT retrieved text word-for-word in your final answer.
    
    Error: [Tool error: Tool "recall" not found] — skip this, use data from other calls
    Produce the output now.
    ── raw response ──
    I am unable to use the `recall` tool as it is not found in my available tools, as indicated by the error: "Tool error: Tool "recall" not found". Therefore, I cannot store or retrieve the note as requested.
  00:01:40.116 INFO  ◉ [think]      4 steps | 5,254 tok | 0.0s
  00:01:40.134 INFO  ◉ [act]        recall (1 tools)
  00:01:45.073 INFO  Execution completed {"taskId":"01KNN69W8CS2FT0WJ4H593NDAH","success":true,"tokensUsed":5254,"cost":0.0008205,"duration":11618}
  00:01:45.073 INFO  ◉ [complete]   ✓ 01KNN69W8CS2FT0WJ4H593NDAH | 5,254 tok | $0.0008 | 11.6s

═══ Spans (15) ═══
  ✓ execution.run (11622.1ms) [7686f435…]
    ✓ execution.phase.bootstrap (2.9ms) [7686f435…]
      ✓ phase.bootstrap.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.strategy-select (1.1ms) [7686f435…]
      ✓ phase.strategy-select.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.think (3785.6ms) [7686f435…]
      ✓ phase.think.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.act (1.6ms) [7686f435…]
      ✓ phase.act.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.observe (1.1ms) [7686f435…]
      ✓ phase.observe.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.memory-flush (4934.6ms) [7686f435…]
      ✓ phase.memory-flush.metrics (0.0ms) [7686f435…]
    ✓ execution.phase.complete (1.0ms) [7686f435…]
      ✓ phase.complete.metrics (0.0ms) [7686f435…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 11.6s   Steps: 4        │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 5,254 │
│ Cost:     ~$0.008                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            3ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               3.8s (4 iter, 43% of time)
├─ ✅  [act]                  1ms (1 tools)
├─ ✅  [observe]              1ms
├─ ✅  [memory-flush]        4.9s
└─ ✅  [complete]             1ms

🔧 Tool Execution (1 called)
└─ ⚠️  recall      1 calls, 1ms avg 1 errors

🧠 Reasoning Signal
├─ Grade: B   Signal: flat   Mean: 0.260   Delta: +0.442
├─ Model stalled — entropy didn't decrease across iterations
├─  iter  0 ███░░░░░░░░░░░░░░░░░ 0.150 →
├─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
├─  iter  2 ███░░░░░░░░░░░░░░░░░ 0.150 →
└─  iter  4 ████████████░░░░░░░░ 0.592 →
   ┈┈┈
└─ 💡 Consider enabling strategy switching (.withReasoning({ enableStrategySwitching: true }))

⚠️  Alerts & Insights
└─ ⚠️  recall had 1 error(s) (100% failure rate)
✗ 11.6s (4 iters, 5254 tok)
    ⚠  MISSING EXPECTED: /paris/ not found in output
    ⚠  MISSING EXPECTED: /capital/ not found in output
  ⊙ [intelligence] Intelligence: simple task early-stop          ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:01:45.157 INFO  Execution started {"taskId":"01KNN6A7NYCWWEQQXBHTSJA7X9","agentId":"test-intelligence--simple-task-early-stop-1775606505089"}
  00:01:45.161 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:01:45.176 INFO  ◉ [strategy]   reactive
  00:01:45.839 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What color is the sky on a clear day?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What color is the sky on a clear day?
    ── raw response ──
    The sky on a clear day is blue.
  00:01:45.841 INFO  ◉ [think]      1 steps | 162 tok | 0.0s
  00:01:45.854 INFO  Execution completed {"taskId":"01KNN6A7NYCWWEQQXBHTSJA7X9","success":true,"tokensUsed":162,"cost":0.000028349999999999998,"duration":697}
  00:01:45.854 INFO  ◉ [complete]   ✓ 01KNN6A7NYCWWEQQXBHTSJA7X9 | 162 tok | $0.0000 | 0.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:01:45.157 INFO  Execution started {"taskId":"01KNN6A7NYCWWEQQXBHTSJA7X9","agentId":"test-intelligence--simple-task-early-stop-1775606505089"}
  00:01:45.161 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:01:45.176 INFO  ◉ [strategy]   reactive
  00:01:45.839 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What color is the sky on a clear day?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What color is the sky on a clear day?
    ── raw response ──
    The sky on a clear day is blue.
  00:01:45.841 INFO  ◉ [think]      1 steps | 162 tok | 0.0s
  00:01:45.854 INFO  Execution completed {"taskId":"01KNN6A7NYCWWEQQXBHTSJA7X9","success":true,"tokensUsed":162,"cost":0.000028349999999999998,"duration":697}
  00:01:45.854 INFO  ◉ [complete]   ✓ 01KNN6A7NYCWWEQQXBHTSJA7X9 | 162 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (698.3ms) [d019b4ed…]
    ✓ execution.phase.bootstrap (2.8ms) [d019b4ed…]
      ✓ phase.bootstrap.metrics (0.0ms) [d019b4ed…]
    ✓ execution.phase.strategy-select (14.8ms) [d019b4ed…]
      ✓ phase.strategy-select.metrics (0.0ms) [d019b4ed…]
    ✓ execution.phase.think (665.0ms) [d019b4ed…]
      ✓ phase.think.metrics (0.0ms) [d019b4ed…]
    ✓ execution.phase.complete (1.5ms) [d019b4ed…]
      ✓ phase.complete.metrics (0.0ms) [d019b4ed…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 697ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 162 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]     14ms
├─ ✅  [think]              665ms (1 iter, 97% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 706ms (1 iters, 162 tok)
  ⊙ [intelligence] Intelligence: moderate task                   ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:01:45.913 INFO  Execution started {"taskId":"01KNN6A8DN6ZDB3V866SMKGASD","agentId":"test-intelligence--moderate-task-1775606505858"}
  00:01:45.915 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:01:45.917 INFO  ◉ [strategy]   reactive
  00:01:54.114 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain how a hash table works, including how collisions are handled.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain how a hash table works, including how collisions are handled.
    ── raw response ──
    A hash table is a data structure used to store key-value pairs. Its primary goal is to provide very fast average-case time complexity for insertion, deletion, and retrieval operations, ideally O(1).
    
    Here's how it works:
    
    1.  **Core Components:**
        *   **Array (Buckets/Slots):** A hash table uses an underlying array to store the data. Each position in this array is often called a "bucket" or "slot."
        *   **Hash Function:** This is a crucial component. When you want to store a key-value pair, the hash function takes the `key` as input and converts it into an integer index (or hash code). This index determines where in the array the value (or a reference to it) should be stored. A good hash function distributes keys evenly across the array to minimize collisions.
    
    2.  **How Data is Stored (Insertion):**
        *   When you want to `insert(key, value)`:
            1.  The `key` is passed to the hash function.
            2.  The hash function computes an integer hash code.
            3.  This hash code is then typically mapped to a valid index within the bounds of the array (e.g., using the modulo operator: `index = hash_code % array_size`).
            4.  The `value` (or the key-value pair itself) is then stored at that `index` in the array.
    
    3.  **How Data is Retrieved (Lookup):**
        *   When you want to `retrieve(key)`:
            1.  The same `key` is passed to the *same* hash function.
            2.  The hash function computes the *same* hash code.
            3.  This hash code is mapped to the *same* index.
            4.  The hash table then looks at the data stored at that `index` in the array and returns the corresponding `value`.
    
    4.  **Collisions:**
        *   A **collision** occurs when two different keys produce the *same* hash code, and thus map to the *same* index in the array. This is inevitable with a finite-sized array and an infinite (or very large) set of possible keys, even with a good hash function.
        *   When a collision happens, we cannot simply overwrite existing data. Collision handling strategies are essential to ensure all key-value pairs can be stored and retrieved correctly.
    
    5.  **Collision Handling Strategies:**
    
        *   **a) Separate Chaining:**
            *   **Concept:** Instead of storing a single key-value pair directly in each array bucket, each bucket stores a reference to a *secondary data structure*, typically a linked list (though other structures like balanced trees can also be used for very large chains).
            *   **How it works:** When a collision occurs, the new key-value pair is simply added to the linked list at the calculated index.
            *   **Insertion:** Hash the key, go to the index, and add the new key-value pair to the linked list at that index.
            *   **Retrieval:** Hash the key, go to the index, and then traverse the linked list at that index to find the matching key.
            *   **Advantages:** Relatively simple to implement, less sensitive to load factor (can handle more elements than buckets), deletion is straightforward.
            *   **Disadvantages:** Requires extra memory for pointers in the linked lists, can degrade to O(N) worst-case if all keys hash to the same bucket (a very long linked list).
    
        *   **b) Open Addressing (or Closed Hashing):**
            *   **Concept:** When a collision occurs, instead of storing the new key-value pair externally, the hash table searches for another *empty slot* within the *same array*.
            *   **How it works:** A "probing sequence" is used to find the next available slot.
            *   **Insertion:** Hash the key to get an initial index. If that slot is occupied, probe for the next available slot according to a predefined strategy.
            *   **Retrieval:** Hash the key to get an initial index. If the key at that slot doesn't match, follow the *same probing sequence* until the key is found or an empty slot is encountered (meaning the key is not present).
            *   **Deletion:** More complex than chaining. Often involves marking a slot as "deleted" rather than truly emptying it, to avoid breaking the probing sequence for other keys.
            *   **Types of Probing:**
                *   **i. Linear Probing:** If `index` is occupied, try `index + 1`, then `index + 2`, `index + 3`, and so on (modulo `array_size`).
                    *   **Issue:** Can lead to "primary clustering," where long runs of occupied slots form, increasing search times.
                *   **ii. Quadratic Probing:** If `index` is occupied, try `index + 1^2`, then `index + 2^2`, `index + 3^2`, and so on (modulo `array_size`).
                    *   **Issue:** Can lead to "secondary clustering," where keys that initially hash to the same location follow the same probing sequence.
                *   **iii. Double Hashing:** Uses a *second hash function* to determine the step size for probing. If `index` is occupied, try `index + h2(key)`, then `index + 2 * h2(key)`, `index + 3 * h2(key)`, etc. (modulo `array_size`). `h2(key)` must never return zero.
                    *   **Advantages:** Distributes keys more uniformly, reduces clustering significantly.
                    *   **Disadvantages:** Requires a second hash function.
            *   **Advantages of Open Addressing:** Better cache performance (data is contiguous in memory), no extra memory for pointers.
            *   **Disadvantages of Open Addressing:** Sensitive to load factor (performance degrades quickly as the table fills), deletion is complex, can suffer from clustering.
    
    The choice of hash function and collision resolution strategy significantly impacts the performance of a hash table. A good hash function minimizes collisions, and an efficient collision resolution strategy handles the inevitable collisions gracefully, maintaining fast average-case performance.
  00:01:54.116 INFO  ◉ [think]      1 steps | 1,510 tok | 0.0s
  00:01:54.129 INFO  Execution completed {"taskId":"01KNN6A8DN6ZDB3V866SMKGASD","success":true,"tokensUsed":1510,"cost":0.0008344499999999999,"duration":8216}
  00:01:54.129 INFO  ◉ [complete]   ✓ 01KNN6A8DN6ZDB3V866SMKGASD | 1,510 tok | $0.0008 | 8.2s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:01:45.913 INFO  Execution started {"taskId":"01KNN6A8DN6ZDB3V866SMKGASD","agentId":"test-intelligence--moderate-task-1775606505858"}
  00:01:45.915 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:01:45.917 INFO  ◉ [strategy]   reactive
  00:01:54.114 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain how a hash table works, including how collisions are handled.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain how a hash table works, including how collisions are handled.
    ── raw response ──
    A hash table is a data structure used to store key-value pairs. Its primary goal is to provide very fast average-case time complexity for insertion, deletion, and retrieval operations, ideally O(1).
    
    Here's how it works:
    
    1.  **Core Components:**
        *   **Array (Buckets/Slots):** A hash table uses an underlying array to store the data. Each position in this array is often called a "bucket" or "slot."
        *   **Hash Function:** This is a crucial component. When you want to store a key-value pair, the hash function takes the `key` as input and converts it into an integer index (or hash code). This index determines where in the array the value (or a reference to it) should be stored. A good hash function distributes keys evenly across the array to minimize collisions.
    
    2.  **How Data is Stored (Insertion):**
        *   When you want to `insert(key, value)`:
            1.  The `key` is passed to the hash function.
            2.  The hash function computes an integer hash code.
            3.  This hash code is then typically mapped to a valid index within the bounds of the array (e.g., using the modulo operator: `index = hash_code % array_size`).
            4.  The `value` (or the key-value pair itself) is then stored at that `index` in the array.
    
    3.  **How Data is Retrieved (Lookup):**
        *   When you want to `retrieve(key)`:
            1.  The same `key` is passed to the *same* hash function.
            2.  The hash function computes the *same* hash code.
            3.  This hash code is mapped to the *same* index.
            4.  The hash table then looks at the data stored at that `index` in the array and returns the corresponding `value`.
    
    4.  **Collisions:**
        *   A **collision** occurs when two different keys produce the *same* hash code, and thus map to the *same* index in the array. This is inevitable with a finite-sized array and an infinite (or very large) set of possible keys, even with a good hash function.
        *   When a collision happens, we cannot simply overwrite existing data. Collision handling strategies are essential to ensure all key-value pairs can be stored and retrieved correctly.
    
    5.  **Collision Handling Strategies:**
    
        *   **a) Separate Chaining:**
            *   **Concept:** Instead of storing a single key-value pair directly in each array bucket, each bucket stores a reference to a *secondary data structure*, typically a linked list (though other structures like balanced trees can also be used for very large chains).
            *   **How it works:** When a collision occurs, the new key-value pair is simply added to the linked list at the calculated index.
            *   **Insertion:** Hash the key, go to the index, and add the new key-value pair to the linked list at that index.
            *   **Retrieval:** Hash the key, go to the index, and then traverse the linked list at that index to find the matching key.
            *   **Advantages:** Relatively simple to implement, less sensitive to load factor (can handle more elements than buckets), deletion is straightforward.
            *   **Disadvantages:** Requires extra memory for pointers in the linked lists, can degrade to O(N) worst-case if all keys hash to the same bucket (a very long linked list).
    
        *   **b) Open Addressing (or Closed Hashing):**
            *   **Concept:** When a collision occurs, instead of storing the new key-value pair externally, the hash table searches for another *empty slot* within the *same array*.
            *   **How it works:** A "probing sequence" is used to find the next available slot.
            *   **Insertion:** Hash the key to get an initial index. If that slot is occupied, probe for the next available slot according to a predefined strategy.
            *   **Retrieval:** Hash the key to get an initial index. If the key at that slot doesn't match, follow the *same probing sequence* until the key is found or an empty slot is encountered (meaning the key is not present).
            *   **Deletion:** More complex than chaining. Often involves marking a slot as "deleted" rather than truly emptying it, to avoid breaking the probing sequence for other keys.
            *   **Types of Probing:**
                *   **i. Linear Probing:** If `index` is occupied, try `index + 1`, then `index + 2`, `index + 3`, and so on (modulo `array_size`).
                    *   **Issue:** Can lead to "primary clustering," where long runs of occupied slots form, increasing search times.
                *   **ii. Quadratic Probing:** If `index` is occupied, try `index + 1^2`, then `index + 2^2`, `index + 3^2`, and so on (modulo `array_size`).
                    *   **Issue:** Can lead to "secondary clustering," where keys that initially hash to the same location follow the same probing sequence.
                *   **iii. Double Hashing:** Uses a *second hash function* to determine the step size for probing. If `index` is occupied, try `index + h2(key)`, then `index + 2 * h2(key)`, `index + 3 * h2(key)`, etc. (modulo `array_size`). `h2(key)` must never return zero.
                    *   **Advantages:** Distributes keys more uniformly, reduces clustering significantly.
                    *   **Disadvantages:** Requires a second hash function.
            *   **Advantages of Open Addressing:** Better cache performance (data is contiguous in memory), no extra memory for pointers.
            *   **Disadvantages of Open Addressing:** Sensitive to load factor (performance degrades quickly as the table fills), deletion is complex, can suffer from clustering.
    
    The choice of hash function and collision resolution strategy significantly impacts the performance of a hash table. A good hash function minimizes collisions, and an efficient collision resolution strategy handles the inevitable collisions gracefully, maintaining fast average-case performance.
  00:01:54.116 INFO  ◉ [think]      1 steps | 1,510 tok | 0.0s
  00:01:54.129 INFO  Execution completed {"taskId":"01KNN6A8DN6ZDB3V866SMKGASD","success":true,"tokensUsed":1510,"cost":0.0008344499999999999,"duration":8216}
  00:01:54.129 INFO  ◉ [complete]   ✓ 01KNN6A8DN6ZDB3V866SMKGASD | 1,510 tok | $0.0008 | 8.2s

═══ Spans (9) ═══
  ✓ execution.run (8219.4ms) [59b8f6a2…]
    ✓ execution.phase.bootstrap (1.6ms) [59b8f6a2…]
      ✓ phase.bootstrap.metrics (0.0ms) [59b8f6a2…]
    ✓ execution.phase.strategy-select (1.1ms) [59b8f6a2…]
      ✓ phase.strategy-select.metrics (0.0ms) [59b8f6a2…]
    ✓ execution.phase.think (8199.1ms) [59b8f6a2…]
      ✓ phase.think.metrics (0.0ms) [59b8f6a2…]
    ✓ execution.phase.complete (0.8ms) [59b8f6a2…]
      ✓ phase.complete.metrics (0.0ms) [59b8f6a2…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 8.2s   Steps: 1         │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 1,510 │
│ Cost:     ~$0.002                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               8.2s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 8.2s (1 iters, 1510 tok)
  ⊙ [intelligence] Intelligence: with memory + debrief           ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:01:54.203 INFO  Execution started {"taskId":"01KNN6AGGP703KD89R157GEET3","agentId":"test-intelligence--with-memory---debrief-1775606514139"}
  00:01:54.222 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 19ms
  00:01:54.223 INFO  ◉ [strategy]   reactive
  00:01:59.043 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Describe the observer design pattern. Include: intent, structure, when to use it, and a brief code example.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Describe the observer design pattern. Include: intent, structure, when to use it, and a brief code example.
    ── raw response ──
    The Observer design pattern is a behavioral design pattern that defines a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically.
    
    **Intent:**
    To define a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically. It promotes loose coupling between the subject (the object being observed) and its observers (the objects observing it).
    
    **Structure:**
    
    *   **Subject (Publisher):**
        *   Maintains a list of its dependent observers.
        *   Provides an interface for attaching (registering) and detaching (unregistering) observer objects.
        *   Notifies all registered observers when its state changes.
    *   **Observer (Subscriber):**
        *   Defines an updating interface for objects that should be notified of changes in a subject.
    *   **ConcreteSubject:**
        *   Stores the state of interest to ConcreteObserver objects.
        *   Sends a notification to its observers when its state changes.
    *   **ConcreteObserver:**
        *   Maintains a reference to a ConcreteSubject object.
        *   Stores state that should stay consistent with the subject's.
        *   Implements the Observer updating interface to keep its state consistent with the subject's.
    
    **When to use it:**
    
    *   **When a change to one object requires changing others, and you don't know how many objects need to be changed.** The Observer pattern allows the subject to notify an arbitrary number of observers without hardcoding their identities.
    *   **When an object should be able to notify other objects without making assumptions about who these other objects are.** This promotes loose coupling.
    *   **When the objects that need to be notified can change dynamically.** Observers can be added or removed at runtime.
    *   **For event handling systems.** Many GUI frameworks use the Observer pattern (or variations like event listeners) to handle user interactions.
    
    **Brief Code Example (Python):**
    
    ```python
    # Subject (Publisher)
    class Subject:
        def __init__(self):
            self._observers = []
            self._state = None
    
        def attach(self, observer):
            if observer not in self._observers:
                self._observers.append(observer)
    
        def detach(self, observer):
            try:
                self._observers.remove(observer)
            except ValueError:
                pass # Observer not found
    
        def notify(self):
            for observer in self._observers:
                observer.update(self._state)
    
        @property
        def state(self):
            return self._state
    
        @state.setter
        def state(self, new_state):
            self._state = new_state
            self.notify() # Notify observers when state changes
    
    # Observer (Subscriber)
    class Observer:
        def update(self, state):
            raise NotImplementedError("Subclasses must implement update method")
    
    # Concrete Observers
    class ConcreteObserverA(Observer):
        def __init__(self, name):
            self._name = name
            self._subject_state = None
    
        def update(self, state):
            self._subject_state = state
            print(f"Observer {self._name}: Subject state updated to {self._subject_state}")
    
    class ConcreteObserverB(Observer):
        def __init__(self, name):
            self._name = name
            self._subject_state = None
    
        def update(self, state):
            self._subject_state = state
            print(f"Observer {self._name}: Received update. New state is {self._subject_state}")
    
    # Client Code
    if __name__ == "__main__":
        subject = Subject()
    
        observer1 = ConcreteObserverA("Observer 1")
        observer2 = ConcreteObserverB("Observer 2")
        observer3 = ConcreteObserverA("Observer 3")
    
        subject.attach(observer1)
        subject.attach(observer2)
        subject.attach(observer3)
    
        print("--- Changing subject state to 'State 1' ---")
        subject.state = "State 1"
    
        print("\n--- Detaching Observer 2 ---")
        subject.detach(observer2)
    
        print("\n--- Changing subject state to 'State 2' ---")
        subject.state = "State 2"
    ```
  00:01:59.046 INFO  ◉ [think]      1 steps | 1,128 tok | 0.0s
  00:02:02.079 INFO  Execution completed {"taskId":"01KNN6AGGP703KD89R157GEET3","success":true,"tokensUsed":1128,"cost":0.00059625,"duration":4850}
  00:02:02.079 INFO  ◉ [complete]   ✓ 01KNN6AGGP703KD89R157GEET3 | 1,128 tok | $0.0006 | 4.8s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:01:54.203 INFO  Execution started {"taskId":"01KNN6AGGP703KD89R157GEET3","agentId":"test-intelligence--with-memory---debrief-1775606514139"}
  00:01:54.222 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 19ms
  00:01:54.223 INFO  ◉ [strategy]   reactive
  00:01:59.043 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:01 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Describe the observer design pattern. Include: intent, structure, when to use it, and a brief code example.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Describe the observer design pattern. Include: intent, structure, when to use it, and a brief code example.
    ── raw response ──
    The Observer design pattern is a behavioral design pattern that defines a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically.
    
    **Intent:**
    To define a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically. It promotes loose coupling between the subject (the object being observed) and its observers (the objects observing it).
    
    **Structure:**
    
    *   **Subject (Publisher):**
        *   Maintains a list of its dependent observers.
        *   Provides an interface for attaching (registering) and detaching (unregistering) observer objects.
        *   Notifies all registered observers when its state changes.
    *   **Observer (Subscriber):**
        *   Defines an updating interface for objects that should be notified of changes in a subject.
    *   **ConcreteSubject:**
        *   Stores the state of interest to ConcreteObserver objects.
        *   Sends a notification to its observers when its state changes.
    *   **ConcreteObserver:**
        *   Maintains a reference to a ConcreteSubject object.
        *   Stores state that should stay consistent with the subject's.
        *   Implements the Observer updating interface to keep its state consistent with the subject's.
    
    **When to use it:**
    
    *   **When a change to one object requires changing others, and you don't know how many objects need to be changed.** The Observer pattern allows the subject to notify an arbitrary number of observers without hardcoding their identities.
    *   **When an object should be able to notify other objects without making assumptions about who these other objects are.** This promotes loose coupling.
    *   **When the objects that need to be notified can change dynamically.** Observers can be added or removed at runtime.
    *   **For event handling systems.** Many GUI frameworks use the Observer pattern (or variations like event listeners) to handle user interactions.
    
    **Brief Code Example (Python):**
    
    ```python
    # Subject (Publisher)
    class Subject:
        def __init__(self):
            self._observers = []
            self._state = None
    
        def attach(self, observer):
            if observer not in self._observers:
                self._observers.append(observer)
    
        def detach(self, observer):
            try:
                self._observers.remove(observer)
            except ValueError:
                pass # Observer not found
    
        def notify(self):
            for observer in self._observers:
                observer.update(self._state)
    
        @property
        def state(self):
            return self._state
    
        @state.setter
        def state(self, new_state):
            self._state = new_state
            self.notify() # Notify observers when state changes
    
    # Observer (Subscriber)
    class Observer:
        def update(self, state):
            raise NotImplementedError("Subclasses must implement update method")
    
    # Concrete Observers
    class ConcreteObserverA(Observer):
        def __init__(self, name):
            self._name = name
            self._subject_state = None
    
        def update(self, state):
            self._subject_state = state
            print(f"Observer {self._name}: Subject state updated to {self._subject_state}")
    
    class ConcreteObserverB(Observer):
        def __init__(self, name):
            self._name = name
            self._subject_state = None
    
        def update(self, state):
            self._subject_state = state
            print(f"Observer {self._name}: Received update. New state is {self._subject_state}")
    
    # Client Code
    if __name__ == "__main__":
        subject = Subject()
    
        observer1 = ConcreteObserverA("Observer 1")
        observer2 = ConcreteObserverB("Observer 2")
        observer3 = ConcreteObserverA("Observer 3")
    
        subject.attach(observer1)
        subject.attach(observer2)
        subject.attach(observer3)
    
        print("--- Changing subject state to 'State 1' ---")
        subject.state = "State 1"
    
        print("\n--- Detaching Observer 2 ---")
        subject.detach(observer2)
    
        print("\n--- Changing subject state to 'State 2' ---")
        subject.state = "State 2"
    ```
  00:01:59.046 INFO  ◉ [think]      1 steps | 1,128 tok | 0.0s
  00:02:02.079 INFO  Execution completed {"taskId":"01KNN6AGGP703KD89R157GEET3","success":true,"tokensUsed":1128,"cost":0.00059625,"duration":4850}
  00:02:02.079 INFO  ◉ [complete]   ✓ 01KNN6AGGP703KD89R157GEET3 | 1,128 tok | $0.0006 | 4.8s

═══ Spans (9) ═══
  ✓ execution.run (7878.8ms) [9d264e39…]
    ✓ execution.phase.bootstrap (18.4ms) [9d264e39…]
      ✓ phase.bootstrap.metrics (0.0ms) [9d264e39…]
    ✓ execution.phase.strategy-select (0.9ms) [9d264e39…]
      ✓ phase.strategy-select.metrics (0.0ms) [9d264e39…]
    ✓ execution.phase.think (4822.6ms) [9d264e39…]
      ✓ phase.think.metrics (0.0ms) [9d264e39…]
    ✓ execution.phase.complete (0.9ms) [9d264e39…]
      ✓ phase.complete.metrics (0.0ms) [9d264e39…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 4.8s   Steps: 1         │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 1,128 │
│ Cost:     ~$0.002                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               4.8s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 7.9s (1 iters, 1128 tok)
  ⊙ [robustness  ] Empty-ish input handling                      ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:02.158 INFO  Execution started {"taskId":"01KNN6AR99BAZ2CQJFJZ8A6HB6","agentId":"test-empty-ish-input-handling-1775606522103"}
  00:02:02.161 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:02.162 INFO  ◉ [strategy]   reactive
  00:02:02.809 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Hi
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Hi
    ── raw response ──
    Hello! How can I help you today?
  00:02:02.810 INFO  ◉ [think]      1 steps | 144 tok | 0.0s
  00:02:02.831 INFO  Execution completed {"taskId":"01KNN6AR99BAZ2CQJFJZ8A6HB6","success":true,"tokensUsed":144,"cost":0.00002565,"duration":673}
  00:02:02.831 INFO  ◉ [complete]   ✓ 01KNN6AR99BAZ2CQJFJZ8A6HB6 | 144 tok | $0.0000 | 0.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:02.158 INFO  Execution started {"taskId":"01KNN6AR99BAZ2CQJFJZ8A6HB6","agentId":"test-empty-ish-input-handling-1775606522103"}
  00:02:02.161 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:02.162 INFO  ◉ [strategy]   reactive
  00:02:02.809 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Hi
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Hi
    ── raw response ──
    Hello! How can I help you today?
  00:02:02.810 INFO  ◉ [think]      1 steps | 144 tok | 0.0s
  00:02:02.831 INFO  Execution completed {"taskId":"01KNN6AR99BAZ2CQJFJZ8A6HB6","success":true,"tokensUsed":144,"cost":0.00002565,"duration":673}
  00:02:02.831 INFO  ◉ [complete]   ✓ 01KNN6AR99BAZ2CQJFJZ8A6HB6 | 144 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (673.4ms) [117deec3…]
    ✓ execution.phase.bootstrap (1.7ms) [117deec3…]
      ✓ phase.bootstrap.metrics (0.0ms) [117deec3…]
    ✓ execution.phase.strategy-select (1.1ms) [117deec3…]
      ✓ phase.strategy-select.metrics (0.0ms) [117deec3…]
    ✓ execution.phase.think (648.3ms) [117deec3…]
      ✓ phase.think.metrics (0.0ms) [117deec3…]
    ✓ execution.phase.complete (0.8ms) [117deec3…]
      ✓ phase.complete.metrics (0.0ms) [117deec3…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 673ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 144 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              648ms (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 679ms (1 iters, 144 tok)
  ⊙ [robustness  ] Instruction following: format constraint      ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:02.927 INFO  Execution started {"taskId":"01KNN6AS195XWSKDNWE1VJKC4J","agentId":"test-instruction-following--format-constraint-1775606522838"}
  00:02:02.932 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 5ms
  00:02:02.934 INFO  ◉ [strategy]   reactive
  00:02:03.772 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: List exactly 3 benefits of exercise. Number them 1, 2, 3.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] List exactly 3 benefits of exercise. Number them 1, 2, 3.
    ── raw response ──
    1. Improves cardiovascular health
    2. Boosts mood and reduces stress
    3. Strengthens muscles and bones
  00:02:03.774 INFO  ◉ [think]      1 steps | 193 tok | 0.0s
  00:02:03.789 INFO  Execution completed {"taskId":"01KNN6AS195XWSKDNWE1VJKC4J","success":true,"tokensUsed":193,"cost":0.000038849999999999996,"duration":861}
  00:02:03.789 INFO  ◉ [complete]   ✓ 01KNN6AS195XWSKDNWE1VJKC4J | 193 tok | $0.0000 | 0.9s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:02.927 INFO  Execution started {"taskId":"01KNN6AS195XWSKDNWE1VJKC4J","agentId":"test-instruction-following--format-constraint-1775606522838"}
  00:02:02.932 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 5ms
  00:02:02.934 INFO  ◉ [strategy]   reactive
  00:02:03.772 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: List exactly 3 benefits of exercise. Number them 1, 2, 3.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] List exactly 3 benefits of exercise. Number them 1, 2, 3.
    ── raw response ──
    1. Improves cardiovascular health
    2. Boosts mood and reduces stress
    3. Strengthens muscles and bones
  00:02:03.774 INFO  ◉ [think]      1 steps | 193 tok | 0.0s
  00:02:03.789 INFO  Execution completed {"taskId":"01KNN6AS195XWSKDNWE1VJKC4J","success":true,"tokensUsed":193,"cost":0.000038849999999999996,"duration":861}
  00:02:03.789 INFO  ◉ [complete]   ✓ 01KNN6AS195XWSKDNWE1VJKC4J | 193 tok | $0.0000 | 0.9s

═══ Spans (9) ═══
  ✓ execution.run (863.0ms) [f6e6fd54…]
    ✓ execution.phase.bootstrap (4.7ms) [f6e6fd54…]
      ✓ phase.bootstrap.metrics (0.0ms) [f6e6fd54…]
    ✓ execution.phase.strategy-select (1.5ms) [f6e6fd54…]
      ✓ phase.strategy-select.metrics (0.0ms) [f6e6fd54…]
    ✓ execution.phase.think (840.0ms) [f6e6fd54…]
      ✓ phase.think.metrics (0.0ms) [f6e6fd54…]
    ✓ execution.phase.complete (1.0ms) [f6e6fd54…]
      ✓ phase.complete.metrics (0.0ms) [f6e6fd54…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 861ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 193 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            4ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              840ms (1 iter, 99% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 870ms (1 iters, 193 tok)
  ⊙ [robustness  ] Multi-part question                           ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:03.854 INFO  Execution started {"taskId":"01KNN6ASY81948S5XWXYFY2CBY","agentId":"test-multi-part-question-1775606523793"}
  00:02:03.856 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:03.858 INFO  ◉ [strategy]   reactive
  00:02:04.860 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is the largest ocean? What is the smallest continent? Answer both.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the largest ocean? What is the smallest continent? Answer both.
    ── raw response ──
    The largest ocean is the Pacific Ocean. The smallest continent is Australia.
  00:02:04.862 INFO  ◉ [think]      1 steps | 177 tok | 0.0s
  00:02:04.874 INFO  Execution completed {"taskId":"01KNN6ASY81948S5XWXYFY2CBY","success":true,"tokensUsed":177,"cost":0.00003285,"duration":1021}
  00:02:04.874 INFO  ◉ [complete]   ✓ 01KNN6ASY81948S5XWXYFY2CBY | 177 tok | $0.0000 | 1.0s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:03.854 INFO  Execution started {"taskId":"01KNN6ASY81948S5XWXYFY2CBY","agentId":"test-multi-part-question-1775606523793"}
  00:02:03.856 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:03.858 INFO  ◉ [strategy]   reactive
  00:02:04.860 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is the largest ocean? What is the smallest continent? Answer both.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the largest ocean? What is the smallest continent? Answer both.
    ── raw response ──
    The largest ocean is the Pacific Ocean. The smallest continent is Australia.
  00:02:04.862 INFO  ◉ [think]      1 steps | 177 tok | 0.0s
  00:02:04.874 INFO  Execution completed {"taskId":"01KNN6ASY81948S5XWXYFY2CBY","success":true,"tokensUsed":177,"cost":0.00003285,"duration":1021}
  00:02:04.874 INFO  ◉ [complete]   ✓ 01KNN6ASY81948S5XWXYFY2CBY | 177 tok | $0.0000 | 1.0s

═══ Spans (9) ═══
  ✓ execution.run (1021.3ms) [7d3b21a0…]
    ✓ execution.phase.bootstrap (2.0ms) [7d3b21a0…]
      ✓ phase.bootstrap.metrics (0.0ms) [7d3b21a0…]
    ✓ execution.phase.strategy-select (1.3ms) [7d3b21a0…]
      ✓ phase.strategy-select.metrics (0.0ms) [7d3b21a0…]
    ✓ execution.phase.think (1004.1ms) [7d3b21a0…]
      ✓ phase.think.metrics (0.0ms) [7d3b21a0…]
    ✓ execution.phase.complete (0.9ms) [7d3b21a0…]
      ✓ phase.complete.metrics (0.0ms) [7d3b21a0…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.0s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 177 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               1.0s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.0s (1 iters, 177 tok)
  ⊙ [robustness  ] Code with explanation                         ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:04.931 INFO  Execution started {"taskId":"01KNN6ATZYKD950G6B13TR55G6","agentId":"test-code-with-explanation-1775606524878"}
  00:02:04.934 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:04.949 INFO  ◉ [strategy]   reactive
  00:02:05.946 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a function that reverses a string. Include the complete code and a one-line explanation of how it works.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a function that reverses a string. Include the complete code and a one-line explanation of how it works.
    ── raw response ──
    ```python
    def reverse_string(s: str) -> str:
      """
      Reverses a given string.
      """
      return s[::-1]
    
    # This function reverses a string by using extended slice syntax to create a reversed copy.
    ```
  00:02:05.948 INFO  ◉ [think]      1 steps | 233 tok | 0.0s
  00:02:05.961 INFO  Execution completed {"taskId":"01KNN6ATZYKD950G6B13TR55G6","success":true,"tokensUsed":233,"cost":0.00005924999999999999,"duration":1031}
  00:02:05.961 INFO  ◉ [complete]   ✓ 01KNN6ATZYKD950G6B13TR55G6 | 233 tok | $0.0001 | 1.0s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:04.931 INFO  Execution started {"taskId":"01KNN6ATZYKD950G6B13TR55G6","agentId":"test-code-with-explanation-1775606524878"}
  00:02:04.934 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:04.949 INFO  ◉ [strategy]   reactive
  00:02:05.946 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a function that reverses a string. Include the complete code and a one-line explanation of how it works.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a function that reverses a string. Include the complete code and a one-line explanation of how it works.
    ── raw response ──
    ```python
    def reverse_string(s: str) -> str:
      """
      Reverses a given string.
      """
      return s[::-1]
    
    # This function reverses a string by using extended slice syntax to create a reversed copy.
    ```
  00:02:05.948 INFO  ◉ [think]      1 steps | 233 tok | 0.0s
  00:02:05.961 INFO  Execution completed {"taskId":"01KNN6ATZYKD950G6B13TR55G6","success":true,"tokensUsed":233,"cost":0.00005924999999999999,"duration":1031}
  00:02:05.961 INFO  ◉ [complete]   ✓ 01KNN6ATZYKD950G6B13TR55G6 | 233 tok | $0.0001 | 1.0s

═══ Spans (9) ═══
  ✓ execution.run (1031.2ms) [5af3300d…]
    ✓ execution.phase.bootstrap (3.0ms) [5af3300d…]
      ✓ phase.bootstrap.metrics (0.0ms) [5af3300d…]
    ✓ execution.phase.strategy-select (14.4ms) [5af3300d…]
      ✓ phase.strategy-select.metrics (0.0ms) [5af3300d…]
    ✓ execution.phase.think (998.6ms) [5af3300d…]
      ✓ phase.think.metrics (0.0ms) [5af3300d…]
    ✓ execution.phase.complete (0.9ms) [5af3300d…]
      ✓ phase.complete.metrics (0.0ms) [5af3300d…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.0s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 233 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            3ms
├─ ✅  [strategy-select]     14ms
├─ ✅  [think]              998ms (1 iter, 98% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.0s (1 iters, 233 tok)
  ⊙ [robustness  ] Ambiguous request: graceful handling          ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:06.016 INFO  Execution started {"taskId":"01KNN6AW1W8TEF9NK8ZA7DDAY2","agentId":"test-ambiguous-request--graceful-handling-1775606525964"}
  00:02:06.018 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:06.032 INFO  ◉ [strategy]   reactive
  00:02:09.063 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Tell me about Mercury.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Tell me about Mercury.
    ── raw response ──
    Mercury is the smallest planet in our solar system and the closest to the Sun. It is a terrestrial planet, meaning it has a rocky body, much like Earth, Venus, and Mars.
    
    Here are some key facts about Mercury:
    *   **Size:** It's only slightly larger than Earth's Moon.
    *   **Orbit:** It has the shortest orbital period of all the planets, completing a trip around the Sun in just 88 Earth days.
    *   **Rotation:** Despite its fast orbit, Mercury rotates very slowly. One day on Mercury (from sunrise to sunrise) lasts about 176 Earth days.
    *   **Surface:** Its surface is heavily cratered, resembling the Moon, indicating a lack of significant geological activity for billions of years. It also has vast plains and scarps (cliffs), which are thought to be wrinkles formed as the planet cooled and contracted.
    *   **Atmosphere:** Mercury has an extremely thin atmosphere, or exosphere, composed primarily of atoms blasted off its surface by solar wind and micrometeoroid impacts. This means it has no significant atmosphere to trap heat or protect it from impacts.
    *   **Temperature Extremes:** Due to its proximity to the Sun and lack of a substantial atmosphere, Mercury experiences extreme temperature variations. Dayside temperatures can soar to about 430°C (800°F), while nightside temperatures can plummet to around -180°C (-290°F).
    *   **Water Ice:** Surprisingly, despite the extreme heat, radar observations and data from the MESSENGER spacecraft have provided strong evidence for water ice in permanently shadowed craters near Mercury's poles, where temperatures remain consistently cold.
    *   **Core:** It has a relatively large iron core, which makes up about 55% of its volume. This large core is thought to be responsible for Mercury's weak but global magnetic field.
    
    Mercury has been visited by two NASA spacecraft: Mariner 10 in the 1970s and MESSENGER, which orbited the planet from 2011 to 2015. The European-Japanese BepiColombo mission is currently en route to Mercury and is expected to enter orbit in 2025.
  00:02:09.065 INFO  ◉ [think]      1 steps | 611 tok | 0.0s
  00:02:09.076 INFO  Execution completed {"taskId":"01KNN6AW1W8TEF9NK8ZA7DDAY2","success":true,"tokensUsed":611,"cost":0.00030225,"duration":3060}
  00:02:09.076 INFO  ◉ [complete]   ✓ 01KNN6AW1W8TEF9NK8ZA7DDAY2 | 611 tok | $0.0003 | 3.1s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:06.016 INFO  Execution started {"taskId":"01KNN6AW1W8TEF9NK8ZA7DDAY2","agentId":"test-ambiguous-request--graceful-handling-1775606525964"}
  00:02:06.018 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:06.032 INFO  ◉ [strategy]   reactive
  00:02:09.063 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Tell me about Mercury.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Tell me about Mercury.
    ── raw response ──
    Mercury is the smallest planet in our solar system and the closest to the Sun. It is a terrestrial planet, meaning it has a rocky body, much like Earth, Venus, and Mars.
    
    Here are some key facts about Mercury:
    *   **Size:** It's only slightly larger than Earth's Moon.
    *   **Orbit:** It has the shortest orbital period of all the planets, completing a trip around the Sun in just 88 Earth days.
    *   **Rotation:** Despite its fast orbit, Mercury rotates very slowly. One day on Mercury (from sunrise to sunrise) lasts about 176 Earth days.
    *   **Surface:** Its surface is heavily cratered, resembling the Moon, indicating a lack of significant geological activity for billions of years. It also has vast plains and scarps (cliffs), which are thought to be wrinkles formed as the planet cooled and contracted.
    *   **Atmosphere:** Mercury has an extremely thin atmosphere, or exosphere, composed primarily of atoms blasted off its surface by solar wind and micrometeoroid impacts. This means it has no significant atmosphere to trap heat or protect it from impacts.
    *   **Temperature Extremes:** Due to its proximity to the Sun and lack of a substantial atmosphere, Mercury experiences extreme temperature variations. Dayside temperatures can soar to about 430°C (800°F), while nightside temperatures can plummet to around -180°C (-290°F).
    *   **Water Ice:** Surprisingly, despite the extreme heat, radar observations and data from the MESSENGER spacecraft have provided strong evidence for water ice in permanently shadowed craters near Mercury's poles, where temperatures remain consistently cold.
    *   **Core:** It has a relatively large iron core, which makes up about 55% of its volume. This large core is thought to be responsible for Mercury's weak but global magnetic field.
    
    Mercury has been visited by two NASA spacecraft: Mariner 10 in the 1970s and MESSENGER, which orbited the planet from 2011 to 2015. The European-Japanese BepiColombo mission is currently en route to Mercury and is expected to enter orbit in 2025.
  00:02:09.065 INFO  ◉ [think]      1 steps | 611 tok | 0.0s
  00:02:09.076 INFO  Execution completed {"taskId":"01KNN6AW1W8TEF9NK8ZA7DDAY2","success":true,"tokensUsed":611,"cost":0.00030225,"duration":3060}
  00:02:09.076 INFO  ◉ [complete]   ✓ 01KNN6AW1W8TEF9NK8ZA7DDAY2 | 611 tok | $0.0003 | 3.1s

═══ Spans (9) ═══
  ✓ execution.run (3061.4ms) [17f2e162…]
    ✓ execution.phase.bootstrap (1.9ms) [17f2e162…]
      ✓ phase.bootstrap.metrics (0.0ms) [17f2e162…]
    ✓ execution.phase.strategy-select (1.0ms) [17f2e162…]
      ✓ phase.strategy-select.metrics (0.0ms) [17f2e162…]
    ✓ execution.phase.think (3032.6ms) [17f2e162…]
      ✓ phase.think.metrics (0.0ms) [17f2e162…]
    ✓ execution.phase.complete (0.8ms) [17f2e162…]
      ✓ phase.complete.metrics (0.0ms) [17f2e162…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 3.1s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 611 │
│ Cost:     ~$0.001                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               3.0s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 3.1s (1 iters, 611 tok)
  ⊙ [convergence ] Converge: simple math should not loop         ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:09.132 INFO  Execution started {"taskId":"01KNN6AZ38R88PVNJ1Y90KAR0N","agentId":"test-converge--simple-math-should-not-loop-1775606529080"}
  00:02:09.147 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 15ms
  00:02:09.149 INFO  ◉ [strategy]   reactive
  00:02:09.787 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is 15 * 7?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is 15 * 7?
    ── raw response ──
    15 * 7 = 105.
  00:02:09.789 INFO  ◉ [think]      1 steps | 162 tok | 0.0s
  00:02:09.802 INFO  Execution completed {"taskId":"01KNN6AZ38R88PVNJ1Y90KAR0N","success":true,"tokensUsed":162,"cost":0.00002925,"duration":670}
  00:02:09.802 INFO  ◉ [complete]   ✓ 01KNN6AZ38R88PVNJ1Y90KAR0N | 162 tok | $0.0000 | 0.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:09.132 INFO  Execution started {"taskId":"01KNN6AZ38R88PVNJ1Y90KAR0N","agentId":"test-converge--simple-math-should-not-loop-1775606529080"}
  00:02:09.147 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 15ms
  00:02:09.149 INFO  ◉ [strategy]   reactive
  00:02:09.787 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is 15 * 7?
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is 15 * 7?
    ── raw response ──
    15 * 7 = 105.
  00:02:09.789 INFO  ◉ [think]      1 steps | 162 tok | 0.0s
  00:02:09.802 INFO  Execution completed {"taskId":"01KNN6AZ38R88PVNJ1Y90KAR0N","success":true,"tokensUsed":162,"cost":0.00002925,"duration":670}
  00:02:09.802 INFO  ◉ [complete]   ✓ 01KNN6AZ38R88PVNJ1Y90KAR0N | 162 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (671.3ms) [ea30ff1a…]
    ✓ execution.phase.bootstrap (15.1ms) [ea30ff1a…]
      ✓ phase.bootstrap.metrics (0.0ms) [ea30ff1a…]
    ✓ execution.phase.strategy-select (0.9ms) [ea30ff1a…]
      ✓ phase.strategy-select.metrics (0.0ms) [ea30ff1a…]
    ✓ execution.phase.think (640.1ms) [ea30ff1a…]
      ✓ phase.think.metrics (0.0ms) [ea30ff1a…]
    ✓ execution.phase.complete (0.9ms) [ea30ff1a…]
      ✓ phase.complete.metrics (0.0ms) [ea30ff1a…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 670ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 162 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           15ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              640ms (1 iter, 98% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 676ms (1 iters, 162 tok)
  ⊙ [convergence ] Converge: list task should terminate          ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:09.859 INFO  Execution started {"taskId":"01KNN6AZSY2ZJH3WH12J6ABECR","agentId":"test-converge--list-task-should-terminate-1775606529806"}
  00:02:09.862 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:09.864 INFO  ◉ [strategy]   reactive
  00:02:10.550 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: List the 4 seasons of the year.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] List the 4 seasons of the year.
    ── raw response ──
    The 4 seasons of the year are:
    1. Spring
    2. Summer
    3. Autumn (or Fall)
    4. Winter
  00:02:10.551 INFO  ◉ [think]      1 steps | 180 tok | 0.0s
  00:02:10.563 INFO  Execution completed {"taskId":"01KNN6AZSY2ZJH3WH12J6ABECR","success":true,"tokensUsed":180,"cost":0.00004005,"duration":704}
  00:02:10.563 INFO  ◉ [complete]   ✓ 01KNN6AZSY2ZJH3WH12J6ABECR | 180 tok | $0.0000 | 0.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:09.859 INFO  Execution started {"taskId":"01KNN6AZSY2ZJH3WH12J6ABECR","agentId":"test-converge--list-task-should-terminate-1775606529806"}
  00:02:09.862 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 3ms
  00:02:09.864 INFO  ◉ [strategy]   reactive
  00:02:10.550 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: List the 4 seasons of the year.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] List the 4 seasons of the year.
    ── raw response ──
    The 4 seasons of the year are:
    1. Spring
    2. Summer
    3. Autumn (or Fall)
    4. Winter
  00:02:10.551 INFO  ◉ [think]      1 steps | 180 tok | 0.0s
  00:02:10.563 INFO  Execution completed {"taskId":"01KNN6AZSY2ZJH3WH12J6ABECR","success":true,"tokensUsed":180,"cost":0.00004005,"duration":704}
  00:02:10.563 INFO  ◉ [complete]   ✓ 01KNN6AZSY2ZJH3WH12J6ABECR | 180 tok | $0.0000 | 0.7s

═══ Spans (9) ═══
  ✓ execution.run (705.1ms) [f0e73551…]
    ✓ execution.phase.bootstrap (2.2ms) [f0e73551…]
      ✓ phase.bootstrap.metrics (0.0ms) [f0e73551…]
    ✓ execution.phase.strategy-select (1.7ms) [f0e73551…]
      ✓ phase.strategy-select.metrics (0.0ms) [f0e73551…]
    ✓ execution.phase.think (686.8ms) [f0e73551…]
      ✓ phase.think.metrics (0.0ms) [f0e73551…]
    ✓ execution.phase.complete (0.9ms) [f0e73551…]
      ✓ phase.complete.metrics (0.0ms) [f0e73551…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 704ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 180 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              687ms (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 711ms (1 iters, 180 tok)
  ⊙ [convergence ] Converge: opinion question                    ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:10.618 INFO  Execution started {"taskId":"01KNN6B0HQ4Q8EC72RA9MJY8QW","agentId":"test-converge--opinion-question-1775606530566"}
  00:02:10.620 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:10.621 INFO  ◉ [strategy]   reactive
  00:02:11.357 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is a good first programming language to learn and why? Keep it brief.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is a good first programming language to learn and why? Keep it brief.
    ── raw response ──
    Python is often recommended as a good first programming language due to its simple syntax, high readability, and extensive libraries, making it easier for beginners to grasp fundamental concepts and build practical applications quickly.
  00:02:11.359 INFO  ◉ [think]      1 steps | 203 tok | 0.0s
  00:02:11.374 INFO  Execution completed {"taskId":"01KNN6B0HQ4Q8EC72RA9MJY8QW","success":true,"tokensUsed":203,"cost":0.000047550000000000004,"duration":756}
  00:02:11.375 INFO  ◉ [complete]   ✓ 01KNN6B0HQ4Q8EC72RA9MJY8QW | 203 tok | $0.0000 | 0.8s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:10.618 INFO  Execution started {"taskId":"01KNN6B0HQ4Q8EC72RA9MJY8QW","agentId":"test-converge--opinion-question-1775606530566"}
  00:02:10.620 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:10.621 INFO  ◉ [strategy]   reactive
  00:02:11.357 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What is a good first programming language to learn and why? Keep it brief.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is a good first programming language to learn and why? Keep it brief.
    ── raw response ──
    Python is often recommended as a good first programming language due to its simple syntax, high readability, and extensive libraries, making it easier for beginners to grasp fundamental concepts and build practical applications quickly.
  00:02:11.359 INFO  ◉ [think]      1 steps | 203 tok | 0.0s
  00:02:11.374 INFO  Execution completed {"taskId":"01KNN6B0HQ4Q8EC72RA9MJY8QW","success":true,"tokensUsed":203,"cost":0.000047550000000000004,"duration":756}
  00:02:11.375 INFO  ◉ [complete]   ✓ 01KNN6B0HQ4Q8EC72RA9MJY8QW | 203 tok | $0.0000 | 0.8s

═══ Spans (9) ═══
  ✓ execution.run (757.3ms) [5444ed28…]
    ✓ execution.phase.bootstrap (1.2ms) [5444ed28…]
      ✓ phase.bootstrap.metrics (0.0ms) [5444ed28…]
    ✓ execution.phase.strategy-select (0.9ms) [5444ed28…]
      ✓ phase.strategy-select.metrics (0.0ms) [5444ed28…]
    ✓ execution.phase.think (737.9ms) [5444ed28…]
      ✓ phase.think.metrics (0.0ms) [5444ed28…]
    ✓ execution.phase.complete (2.0ms) [5444ed28…]
      ✓ phase.complete.metrics (0.0ms) [5444ed28…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 756ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 203 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              738ms (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 762ms (1 iters, 203 tok)
  ⊙ [convergence ] Converge: no-tool task with tools enabled     ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:11.443 INFO  Execution started {"taskId":"01KNN6B1BA5960M2F7XTNH79WB","agentId":"test-converge--no-tool-task-with-tools-enabled-1775606531379"}
  00:02:11.450 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 7ms
  00:02:11.455 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute
  00:02:14.010 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: What is the speed of light in meters per second? Answer directly from your knowledge.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the speed of light in meters per second? Answer directly from your knowledge.
    ── raw response ──
    The speed of light in a vacuum is approximately 299,792,458 meters per second.
  00:02:14.012 INFO  ◉ [think]      1 steps | 3,070 tok | 0.0s
  00:02:14.024 INFO  Execution completed {"taskId":"01KNN6B1BA5960M2F7XTNH79WB","success":true,"tokensUsed":3070,"cost":0.00047174999999999995,"duration":2581}
  00:02:14.024 INFO  ◉ [complete]   ✓ 01KNN6B1BA5960M2F7XTNH79WB | 3,070 tok | $0.0005 | 2.6s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:11.443 INFO  Execution started {"taskId":"01KNN6B1BA5960M2F7XTNH79WB","agentId":"test-converge--no-tool-task-with-tools-enabled-1775606531379"}
  00:02:11.450 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 7ms
  00:02:11.455 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute
  00:02:14.010 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: What is the speed of light in meters per second? Answer directly from your knowledge.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What is the speed of light in meters per second? Answer directly from your knowledge.
    ── raw response ──
    The speed of light in a vacuum is approximately 299,792,458 meters per second.
  00:02:14.012 INFO  ◉ [think]      1 steps | 3,070 tok | 0.0s
  00:02:14.024 INFO  Execution completed {"taskId":"01KNN6B1BA5960M2F7XTNH79WB","success":true,"tokensUsed":3070,"cost":0.00047174999999999995,"duration":2581}
  00:02:14.024 INFO  ◉ [complete]   ✓ 01KNN6B1BA5960M2F7XTNH79WB | 3,070 tok | $0.0005 | 2.6s

═══ Spans (9) ═══
  ✓ execution.run (2581.8ms) [1ca4a347…]
    ✓ execution.phase.bootstrap (5.8ms) [1ca4a347…]
      ✓ phase.bootstrap.metrics (0.0ms) [1ca4a347…]
    ✓ execution.phase.strategy-select (5.5ms) [1ca4a347…]
      ✓ phase.strategy-select.metrics (0.0ms) [1ca4a347…]
    ✓ execution.phase.think (805.5ms) [1ca4a347…]
      ✓ phase.think.metrics (0.0ms) [1ca4a347…]
    ✓ execution.phase.complete (0.9ms) [1ca4a347…]
      ✓ phase.complete.metrics (0.0ms) [1ca4a347…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 2.6s   Steps: 1         │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 3,070 │
│ Cost:     ~$0.005                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            5ms
├─ ✅  [strategy-select]      5ms
├─ ✅  [think]              805ms (1 iter, 99% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 2.6s (1 iters, 3070 tok)
  ⊙ [strategy    ] ReAct: concise factual answer                 ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:14.080 INFO  Execution started {"taskId":"01KNN6B3XW2RFZ85VTSZAGEA32","agentId":"test-react--concise-factual-answer-1775606534028"}
  00:02:14.115 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 36ms
  00:02:14.116 INFO  ◉ [strategy]   reactive
  00:02:14.641 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What are the three states of matter? Give a one-sentence answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What are the three states of matter? Give a one-sentence answer.
    ── raw response ──
    The three states of matter are solid, liquid, and gas.
  00:02:14.644 INFO  ◉ [think]      1 steps | 176 tok | 0.0s
  00:02:14.656 INFO  Execution completed {"taskId":"01KNN6B3XW2RFZ85VTSZAGEA32","success":true,"tokensUsed":176,"cost":0.00003225,"duration":577}
  00:02:14.656 INFO  ◉ [complete]   ✓ 01KNN6B3XW2RFZ85VTSZAGEA32 | 176 tok | $0.0000 | 0.6s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:14.080 INFO  Execution started {"taskId":"01KNN6B3XW2RFZ85VTSZAGEA32","agentId":"test-react--concise-factual-answer-1775606534028"}
  00:02:14.115 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 36ms
  00:02:14.116 INFO  ◉ [strategy]   reactive
  00:02:14.641 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: What are the three states of matter? Give a one-sentence answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] What are the three states of matter? Give a one-sentence answer.
    ── raw response ──
    The three states of matter are solid, liquid, and gas.
  00:02:14.644 INFO  ◉ [think]      1 steps | 176 tok | 0.0s
  00:02:14.656 INFO  Execution completed {"taskId":"01KNN6B3XW2RFZ85VTSZAGEA32","success":true,"tokensUsed":176,"cost":0.00003225,"duration":577}
  00:02:14.656 INFO  ◉ [complete]   ✓ 01KNN6B3XW2RFZ85VTSZAGEA32 | 176 tok | $0.0000 | 0.6s

═══ Spans (9) ═══
  ✓ execution.run (577.0ms) [50d61ba0…]
    ✓ execution.phase.bootstrap (34.9ms) [50d61ba0…]
      ✓ phase.bootstrap.metrics (0.0ms) [50d61ba0…]
    ✓ execution.phase.strategy-select (0.8ms) [50d61ba0…]
      ✓ phase.strategy-select.metrics (0.0ms) [50d61ba0…]
    ✓ execution.phase.think (527.8ms) [50d61ba0…]
      ✓ phase.think.metrics (0.0ms) [50d61ba0…]
    ✓ execution.phase.complete (0.7ms) [50d61ba0…]
      ✓ phase.complete.metrics (0.0ms) [50d61ba0…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 577ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 176 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           35ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              527ms (1 iter, 94% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 581ms (1 iters, 176 tok)
  ⊙ [strategy    ] Plan-Execute: multi-step synthesis            ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:14.713 INFO  Execution started {"taskId":"01KNN6B4HM19GHDQ6CD3RHHTMD","agentId":"test-plan-execute--multi-step-synthesis-1775606534659"}
  00:02:14.716 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:14.717 INFO  ◉ [strategy]   plan-execute-reflect
  00:02:16.627 DEBUG   ┄ [model-io:structured-output]
    ── system ──
    You are a planning agent. Decompose the goal into structured steps.
    
    Respond with ONLY valid JSON. No markdown, no explanation, no thinking tags.
    ── user ──
    You are a planning agent. Decompose the goal into the MINIMUM number of steps needed.
    
    PLANNING RULES:
    - Use the FEWEST steps possible. Combine related work into one step.
    - Prefer "tool_call" steps — they execute instantly without LLM overhead.
    - Use at most ONE "analysis" step to do all reasoning/writing/composition work.
    - Use {{from_step:sN}} in toolArgs to pass previous step results to tool calls.
    - Never split summarizing, formatting, and composing into separate steps — combine them.
    
    GOAL:
    Create a simple database schema for a blog with users, posts, and comments. Show the tables with their columns and relationships.
    
    AVAILABLE TOOLS:
    None — use "analysis" type steps only.
    
    OUTPUT FORMAT:
    Respond with a JSON object containing a "steps" array. Each step has this schema:
    {
      "title": "string — short name for this step",
      "instruction": "string — what the LLM or tool should do",
      "type": "tool_call" | "analysis" | "composite",
      "toolName": "string (optional) — tool to call if type is tool_call",
      "toolArgs": "object (optional) — ALL required arguments for the tool. Use {{from_step:sN}} to inject the result of a previous step as a string value",
      "toolHints": ["string"] (optional) — tool names available for composite steps",
      "dependsOn": ["string"] (optional) — step IDs that must complete first"
    }
    
    Step types:
    - "tool_call": calls a specific tool (set toolName and toolArgs with ALL required params)
    - "analysis": LLM reasoning/writing (no tool needed)
    - "composite": multi-tool sub-task (set toolHints for available tools)
    
    IMPORTANT for tool_call steps:
    - Include ALL required parameters in toolArgs
    - To use output from a PREVIOUS step as an argument value, use {{from_step:sN}} where N is an EARLIER step number
    - A step can ONLY reference steps that come BEFORE it (e.g., s3 can reference s1 or s2, NOT s3 itself)
    - Example: s3 with {"message": "{{from_step:s2}}"} passes s2's result as the "message" argument
    
    EXAMPLE:
    {
      "steps": [
        {
          "title": "Fetch recent commits",
          "instruction": "Get the last 10 commits from the main branch",
          "type": "tool_call",
          "toolName": "github/list_commits",
          "toolArgs": { "owner": "acme", "repo": "app", "perPage": 10 }
        },
        {
          "title": "Summarize changes",
          "instruction": "Analyze the commits and write a brief summary",
          "type": "analysis",
          "dependsOn": ["s1"]
        },
        {
          "title": "Send summary to user",
          "instruction": "Send the commit summary via messaging",
          "type": "tool_call",
          "toolName": "messaging/send",
          "toolArgs": { "recipient": "user@example.com", "message": "{{from_step:s2}}" },
          "dependsOn": ["s2"]
        }
      ]
    }
    
    JSON only, no explanation:
    
    Respond with ONLY a JSON object matching the schema above. No markdown fences, no explanation.
  00:02:16.642 DEBUG   ┄ [thought]  [PLAN 1] Generated 1 steps:
  s1: Design Blog Database Schema (analysis)
  00:02:16.642 DEBUG   ┄ [thought]  [SCHEDULE] 1 steps sequential
  00:02:16.643 DEBUG   ┄ [action]   [STEP 1/1] s1: Design Blog Database Schema (analysis)
  00:02:20.863 DEBUG   ┄ [obs]      [EXEC s1] ✓ **Database Schema for a Simple Blog**

---

**Tables:**

1.  **`users`**
    *   `user_id` (Primary Key, Integer)
    *   `username` (String, Unique, Not Null)
    *   `email` (String, Unique, Not Null)
    *   `password_hash` (String, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)

2.  **`posts`**
    *   `post_id` (Primary Key, Integer)
    *   `user_id` (Foreign Key referencing `users.user_id`, Integer, Not Null)
    *   `title` (String, Not Null)
    *   `content` (Text, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)
    *   `updated_at` (Timestamp, Default: CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP)

3.  **`comments`**
    *   `comment_id` (Primary Key, Integer)
    *   `post_id` (Foreign Key referencing `posts.post_id`, Integer, Not Null)
    *   `user_id` (Foreign Key referencing `users.user_id`, Integer, Not Null)
    *   `content` (Text, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)

---

**Relationships:**

*   **Users to Posts (One-to-Many):**
    *   A single `user` can create multiple `posts`.
    *   Each `post` is associated with exactly one `user` (via `posts.user_id`).

*   **Users to Comments (One-to-Many):**
    *   A single `user` can write multiple `comments`.
    *   Each `comment` is associated with exactly one `user` (via `comments.user_id`).

*   **Posts to Comments (One-to-Many):**
    *   A single `post` can have multiple `comments`.
    *   Each `comment` is associated with exactly one `post` (via `comments.post_id`).
  00:02:22.653 DEBUG   ┄ [thought]  [REFLECT 1] ✓ SATISFIED SATISFIED: The database schema for a blog with users, posts, and comments is clearly defined, showing tables, columns, and relationships as requested.
  00:02:26.008 INFO  ◉ [think]      4 steps | 3,273 tok | 0.0s
  00:02:28.377 INFO  Execution completed {"taskId":"01KNN6B4HM19GHDQ6CD3RHHTMD","success":true,"tokensUsed":3273,"cost":0.0008357999999999999,"duration":13665}
  00:02:28.378 INFO  ◉ [complete]   ✓ 01KNN6B4HM19GHDQ6CD3RHHTMD | 3,273 tok | $0.0008 | 13.7s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (12) ═══
  00:02:14.713 INFO  Execution started {"taskId":"01KNN6B4HM19GHDQ6CD3RHHTMD","agentId":"test-plan-execute--multi-step-synthesis-1775606534659"}
  00:02:14.716 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:14.717 INFO  ◉ [strategy]   plan-execute-reflect
  00:02:16.627 DEBUG   ┄ [model-io:structured-output]
    ── system ──
    You are a planning agent. Decompose the goal into structured steps.
    
    Respond with ONLY valid JSON. No markdown, no explanation, no thinking tags.
    ── user ──
    You are a planning agent. Decompose the goal into the MINIMUM number of steps needed.
    
    PLANNING RULES:
    - Use the FEWEST steps possible. Combine related work into one step.
    - Prefer "tool_call" steps — they execute instantly without LLM overhead.
    - Use at most ONE "analysis" step to do all reasoning/writing/composition work.
    - Use {{from_step:sN}} in toolArgs to pass previous step results to tool calls.
    - Never split summarizing, formatting, and composing into separate steps — combine them.
    
    GOAL:
    Create a simple database schema for a blog with users, posts, and comments. Show the tables with their columns and relationships.
    
    AVAILABLE TOOLS:
    None — use "analysis" type steps only.
    
    OUTPUT FORMAT:
    Respond with a JSON object containing a "steps" array. Each step has this schema:
    {
      "title": "string — short name for this step",
      "instruction": "string — what the LLM or tool should do",
      "type": "tool_call" | "analysis" | "composite",
      "toolName": "string (optional) — tool to call if type is tool_call",
      "toolArgs": "object (optional) — ALL required arguments for the tool. Use {{from_step:sN}} to inject the result of a previous step as a string value",
      "toolHints": ["string"] (optional) — tool names available for composite steps",
      "dependsOn": ["string"] (optional) — step IDs that must complete first"
    }
    
    Step types:
    - "tool_call": calls a specific tool (set toolName and toolArgs with ALL required params)
    - "analysis": LLM reasoning/writing (no tool needed)
    - "composite": multi-tool sub-task (set toolHints for available tools)
    
    IMPORTANT for tool_call steps:
    - Include ALL required parameters in toolArgs
    - To use output from a PREVIOUS step as an argument value, use {{from_step:sN}} where N is an EARLIER step number
    - A step can ONLY reference steps that come BEFORE it (e.g., s3 can reference s1 or s2, NOT s3 itself)
    - Example: s3 with {"message": "{{from_step:s2}}"} passes s2's result as the "message" argument
    
    EXAMPLE:
    {
      "steps": [
        {
          "title": "Fetch recent commits",
          "instruction": "Get the last 10 commits from the main branch",
          "type": "tool_call",
          "toolName": "github/list_commits",
          "toolArgs": { "owner": "acme", "repo": "app", "perPage": 10 }
        },
        {
          "title": "Summarize changes",
          "instruction": "Analyze the commits and write a brief summary",
          "type": "analysis",
          "dependsOn": ["s1"]
        },
        {
          "title": "Send summary to user",
          "instruction": "Send the commit summary via messaging",
          "type": "tool_call",
          "toolName": "messaging/send",
          "toolArgs": { "recipient": "user@example.com", "message": "{{from_step:s2}}" },
          "dependsOn": ["s2"]
        }
      ]
    }
    
    JSON only, no explanation:
    
    Respond with ONLY a JSON object matching the schema above. No markdown fences, no explanation.
  00:02:16.642 DEBUG   ┄ [thought]  [PLAN 1] Generated 1 steps:
  s1: Design Blog Database Schema (analysis)
  00:02:16.642 DEBUG   ┄ [thought]  [SCHEDULE] 1 steps sequential
  00:02:16.643 DEBUG   ┄ [action]   [STEP 1/1] s1: Design Blog Database Schema (analysis)
  00:02:20.863 DEBUG   ┄ [obs]      [EXEC s1] ✓ **Database Schema for a Simple Blog**

---

**Tables:**

1.  **`users`**
    *   `user_id` (Primary Key, Integer)
    *   `username` (String, Unique, Not Null)
    *   `email` (String, Unique, Not Null)
    *   `password_hash` (String, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)

2.  **`posts`**
    *   `post_id` (Primary Key, Integer)
    *   `user_id` (Foreign Key referencing `users.user_id`, Integer, Not Null)
    *   `title` (String, Not Null)
    *   `content` (Text, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)
    *   `updated_at` (Timestamp, Default: CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP)

3.  **`comments`**
    *   `comment_id` (Primary Key, Integer)
    *   `post_id` (Foreign Key referencing `posts.post_id`, Integer, Not Null)
    *   `user_id` (Foreign Key referencing `users.user_id`, Integer, Not Null)
    *   `content` (Text, Not Null)
    *   `created_at` (Timestamp, Default: CURRENT_TIMESTAMP)

---

**Relationships:**

*   **Users to Posts (One-to-Many):**
    *   A single `user` can create multiple `posts`.
    *   Each `post` is associated with exactly one `user` (via `posts.user_id`).

*   **Users to Comments (One-to-Many):**
    *   A single `user` can write multiple `comments`.
    *   Each `comment` is associated with exactly one `user` (via `comments.user_id`).

*   **Posts to Comments (One-to-Many):**
    *   A single `post` can have multiple `comments`.
    *   Each `comment` is associated with exactly one `post` (via `comments.post_id`).
  00:02:22.653 DEBUG   ┄ [thought]  [REFLECT 1] ✓ SATISFIED SATISFIED: The database schema for a blog with users, posts, and comments is clearly defined, showing tables, columns, and relationships as requested.
  00:02:26.008 INFO  ◉ [think]      4 steps | 3,273 tok | 0.0s
  00:02:28.377 INFO  Execution completed {"taskId":"01KNN6B4HM19GHDQ6CD3RHHTMD","success":true,"tokensUsed":3273,"cost":0.0008357999999999999,"duration":13665}
  00:02:28.378 INFO  ◉ [complete]   ✓ 01KNN6B4HM19GHDQ6CD3RHHTMD | 3,273 tok | $0.0008 | 13.7s

═══ Spans (11) ═══
  ✓ execution.run (13666.3ms) [53dbbbc7…]
    ✓ execution.phase.bootstrap (2.4ms) [53dbbbc7…]
      ✓ phase.bootstrap.metrics (0.1ms) [53dbbbc7…]
    ✓ execution.phase.strategy-select (0.8ms) [53dbbbc7…]
      ✓ phase.strategy-select.metrics (0.0ms) [53dbbbc7…]
    ✓ execution.phase.think (11290.2ms) [53dbbbc7…]
      ✓ phase.think.metrics (0.0ms) [53dbbbc7…]
    ✓ execution.phase.memory-flush (2368.1ms) [53dbbbc7…]
      ✓ phase.memory-flush.metrics (0.0ms) [53dbbbc7…]
    ✓ execution.phase.complete (0.9ms) [53dbbbc7…]
      ✓ phase.complete.metrics (0.0ms) [53dbbbc7…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 13.7s   Steps: 4        │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 3,273 │
│ Cost:     ~$0.005                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ⚠️  [think]              11.3s (4 iter, 83% of time)
├─ ✅  [memory-flush]        2.4s
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: B   Signal: flat   Mean: 0.387   Delta: +0.474
├─ Model stalled — entropy didn't decrease across iterations
├─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
└─  iter  3 ████████████░░░░░░░░ 0.624 →
   ┈┈┈
└─ 💡 Consider enabling strategy switching (.withReasoning({ enableStrategySwitching: true }))

⚠️  Alerts & Insights
└─ ⚠️  think phase blocked ≥10s (LLM latency)
✓ 13.7s (4 iters, 3273 tok)
  ⊙ [strategy    ] Adaptive: picks efficient path                ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:28.436 INFO  Execution started {"taskId":"01KNN6BHYGFJ593ESX92TZTKDP","agentId":"test-adaptive--picks-efficient-path-1775606548381"}
  00:02:28.438 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:28.439 INFO  ◉ [strategy]   adaptive
  00:02:28.441 DEBUG   ┄ [thought]  [ADAPTIVE] Heuristic: reactive
  00:02:29.620 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Convert 72 degrees Fahrenheit to Celsius. Show the formula and result.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Convert 72 degrees Fahrenheit to Celsius. Show the formula and result.
    ── raw response ──
    To convert Fahrenheit to Celsius, use the formula:
    C = (F - 32) * 5/9
    
    Given F = 72 degrees Fahrenheit:
    C = (72 - 32) * 5/9
    C = 40 * 5/9
    C = 200 / 9
    C = 22.22
    
    Result: 72 degrees Fahrenheit is approximately 22.22 degrees Celsius.
  00:02:29.622 INFO  ◉ [think]      2 steps | 261 tok | 0.0s
  00:02:29.635 INFO  Execution completed {"taskId":"01KNN6BHYGFJ593ESX92TZTKDP","success":true,"tokensUsed":261,"cost":0.00008324999999999999,"duration":1199}
  00:02:29.635 INFO  ◉ [complete]   ✓ 01KNN6BHYGFJ593ESX92TZTKDP | 261 tok | $0.0001 | 1.2s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (8) ═══
  00:02:28.436 INFO  Execution started {"taskId":"01KNN6BHYGFJ593ESX92TZTKDP","agentId":"test-adaptive--picks-efficient-path-1775606548381"}
  00:02:28.438 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:28.439 INFO  ◉ [strategy]   adaptive
  00:02:28.441 DEBUG   ┄ [thought]  [ADAPTIVE] Heuristic: reactive
  00:02:29.620 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Convert 72 degrees Fahrenheit to Celsius. Show the formula and result.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Convert 72 degrees Fahrenheit to Celsius. Show the formula and result.
    ── raw response ──
    To convert Fahrenheit to Celsius, use the formula:
    C = (F - 32) * 5/9
    
    Given F = 72 degrees Fahrenheit:
    C = (72 - 32) * 5/9
    C = 40 * 5/9
    C = 200 / 9
    C = 22.22
    
    Result: 72 degrees Fahrenheit is approximately 22.22 degrees Celsius.
  00:02:29.622 INFO  ◉ [think]      2 steps | 261 tok | 0.0s
  00:02:29.635 INFO  Execution completed {"taskId":"01KNN6BHYGFJ593ESX92TZTKDP","success":true,"tokensUsed":261,"cost":0.00008324999999999999,"duration":1199}
  00:02:29.635 INFO  ◉ [complete]   ✓ 01KNN6BHYGFJ593ESX92TZTKDP | 261 tok | $0.0001 | 1.2s

═══ Spans (9) ═══
  ✓ execution.run (1199.8ms) [1f4cff63…]
    ✓ execution.phase.bootstrap (1.3ms) [1f4cff63…]
      ✓ phase.bootstrap.metrics (0.0ms) [1f4cff63…]
    ✓ execution.phase.strategy-select (1.1ms) [1f4cff63…]
      ✓ phase.strategy-select.metrics (0.0ms) [1f4cff63…]
    ✓ execution.phase.think (1182.3ms) [1f4cff63…]
      ✓ phase.think.metrics (0.0ms) [1f4cff63…]
    ✓ execution.phase.complete (1.3ms) [1f4cff63…]
  ✓ phase.complete.metrics (0.0ms) [1f4cff63…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.2s   Steps: 2       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 261 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               1.2s (2 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 1.2s (2 iters, 261 tok)
  ⊙ [output      ] Output: code must be complete (not truncated) ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:29.691 INFO  Execution started {"taskId":"01KNN6BK5QENH6XWYQ57JR30YP","agentId":"test-output--code-must-be-complete--not-truncated--1775606549639"}
  00:02:29.692 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 1ms
  00:02:29.694 INFO  ◉ [strategy]   reactive
  00:02:31.621 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a complete TypeScript function called isPrime that checks if a number is prime. Include the full implementation.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a complete TypeScript function called isPrime that checks if a number is prime. Include the full implementation.
    ── raw response ──
    ```typescript
    function isPrime(num: number): boolean {
        // Numbers less than or equal to 1 are not prime.
        if (num <= 1) {
            return false;
        }
        // 2 is the only even prime number.
        if (num === 2) {
            return true;
        }
        // All other even numbers are not prime.
        if (num % 2 === 0) {
            return false;
        }
        // Check for odd divisors from 3 up to the square root of num.
        // We can increment by 2 because we've already handled even numbers.
        for (let i = 3; i <= Math.sqrt(num); i += 2) {
            if (num % i === 0) {
                return false;
            }
        }
        // If no divisors were found, the number is prime.
        return true;
    }
    ```
  00:02:31.623 INFO  ◉ [think]      1 steps | 385 tok | 0.0s
  00:02:31.636 INFO  Execution completed {"taskId":"01KNN6BK5QENH6XWYQ57JR30YP","success":true,"tokensUsed":385,"cost":0.00015225,"duration":1945}
  00:02:31.636 INFO  ◉ [complete]   ✓ 01KNN6BK5QENH6XWYQ57JR30YP | 385 tok | $0.0002 | 1.9s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:29.691 INFO  Execution started {"taskId":"01KNN6BK5QENH6XWYQ57JR30YP","agentId":"test-output--code-must-be-complete--not-truncated--1775606549639"}
  00:02:29.692 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 1ms
  00:02:29.694 INFO  ◉ [strategy]   reactive
  00:02:31.621 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Write a complete TypeScript function called isPrime that checks if a number is prime. Include the full implementation.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Write a complete TypeScript function called isPrime that checks if a number is prime. Include the full implementation.
    ── raw response ──
    ```typescript
    function isPrime(num: number): boolean {
        // Numbers less than or equal to 1 are not prime.
        if (num <= 1) {
            return false;
        }
        // 2 is the only even prime number.
        if (num === 2) {
            return true;
        }
        // All other even numbers are not prime.
        if (num % 2 === 0) {
            return false;
        }
        // Check for odd divisors from 3 up to the square root of num.
        // We can increment by 2 because we've already handled even numbers.
        for (let i = 3; i <= Math.sqrt(num); i += 2) {
            if (num % i === 0) {
                return false;
            }
        }
        // If no divisors were found, the number is prime.
        return true;
    }
    ```
  00:02:31.623 INFO  ◉ [think]      1 steps | 385 tok | 0.0s
  00:02:31.636 INFO  Execution completed {"taskId":"01KNN6BK5QENH6XWYQ57JR30YP","success":true,"tokensUsed":385,"cost":0.00015225,"duration":1945}
  00:02:31.636 INFO  ◉ [complete]   ✓ 01KNN6BK5QENH6XWYQ57JR30YP | 385 tok | $0.0002 | 1.9s

═══ Spans (9) ═══
  ✓ execution.run (1946.0ms) [c92a3e44…]
    ✓ execution.phase.bootstrap (1.2ms) [c92a3e44…]
      ✓ phase.bootstrap.metrics (0.0ms) [c92a3e44…]
    ✓ execution.phase.strategy-select (1.0ms) [c92a3e44…]
      ✓ phase.strategy-select.metrics (0.0ms) [c92a3e44…]
    ✓ execution.phase.think (1929.5ms) [c92a3e44…]
      ✓ phase.think.metrics (0.0ms) [c92a3e44…]
    ✓ execution.phase.complete (0.8ms) [c92a3e44…]
      ✓ phase.complete.metrics (0.0ms) [c92a3e44…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 1.9s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 385 │
│ Cost:     ~$0.001                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            1ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               1.9s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 2.0s (1 iters, 385 tok)
  ⊙ [output      ] Output: structured data must be complete      ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:31.692 INFO  Execution started {"taskId":"01KNN6BN48CDC36W2G4YHCNT1R","agentId":"test-output--structured-data-must-be-complete-1775606551639"}
  00:02:31.694 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:31.695 INFO  ◉ [strategy]   reactive
  00:02:32.665 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Create a JSON object representing a person with name 'Alice', age 30, and hobbies ['reading', 'hiking']. Return the complete JSON.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Create a JSON object representing a person with name 'Alice', age 30, and hobbies ['reading', 'hiking']. Return the complete JSON.
    ── raw response ──
    ```json
    {
      "name": "Alice",
      "age": 30,
      "hobbies": ["reading", "hiking"]
    }
    ```
  00:02:32.667 INFO  ◉ [think]      1 steps | 230 tok | 0.0s
  00:02:32.679 INFO  Execution completed {"taskId":"01KNN6BN48CDC36W2G4YHCNT1R","success":true,"tokensUsed":230,"cost":0.000051149999999999996,"duration":987}
  00:02:32.679 INFO  ◉ [complete]   ✓ 01KNN6BN48CDC36W2G4YHCNT1R | 230 tok | $0.0001 | 1.0s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:31.692 INFO  Execution started {"taskId":"01KNN6BN48CDC36W2G4YHCNT1R","agentId":"test-output--structured-data-must-be-complete-1775606551639"}
  00:02:31.694 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 2ms
  00:02:31.695 INFO  ◉ [strategy]   reactive
  00:02:32.665 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Create a JSON object representing a person with name 'Alice', age 30, and hobbies ['reading', 'hiking']. Return the complete JSON.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Create a JSON object representing a person with name 'Alice', age 30, and hobbies ['reading', 'hiking']. Return the complete JSON.
    ── raw response ──
    ```json
    {
      "name": "Alice",
      "age": 30,
      "hobbies": ["reading", "hiking"]
    }
    ```
  00:02:32.667 INFO  ◉ [think]      1 steps | 230 tok | 0.0s
  00:02:32.679 INFO  Execution completed {"taskId":"01KNN6BN48CDC36W2G4YHCNT1R","success":true,"tokensUsed":230,"cost":0.000051149999999999996,"duration":987}
  00:02:32.679 INFO  ◉ [complete]   ✓ 01KNN6BN48CDC36W2G4YHCNT1R | 230 tok | $0.0001 | 1.0s

═══ Spans (9) ═══
  ✓ execution.run (988.2ms) [f3ceb2a9…]
    ✓ execution.phase.bootstrap (1.8ms) [f3ceb2a9…]
      ✓ phase.bootstrap.metrics (0.0ms) [f3ceb2a9…]
    ✓ execution.phase.strategy-select (0.9ms) [f3ceb2a9…]
      ✓ phase.strategy-select.metrics (0.0ms) [f3ceb2a9…]
    ✓ execution.phase.think (971.5ms) [f3ceb2a9…]
      ✓ phase.think.metrics (0.0ms) [f3ceb2a9…]
    ✓ execution.phase.complete (0.7ms) [f3ceb2a9…]
      ✓ phase.complete.metrics (0.0ms) [f3ceb2a9…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 987ms   Steps: 1      │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 230 │
│ Cost:     ~$0.000                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              971ms (1 iter, 100% of time)
└─ ✅  [complete]             0ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 992ms (1 iters, 230 tok)
  ⊙ [output      ] Output: explanation with examples             ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:32.734 INFO  Execution started {"taskId":"01KNN6BP4TPSBXRYY9130G338V","agentId":"test-output--explanation-with-examples-1775606552682"}
  00:02:32.738 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:32.739 INFO  ◉ [strategy]   reactive
  00:02:35.327 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain what a closure is in JavaScript with a short code example.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain what a closure is in JavaScript with a short code example.
    ── raw response ──
    A closure in JavaScript is a function that remembers its outer variables and can access them even after the outer function has finished executing. This means a closure "closes over" the environment in which it was created.
    
    Here's a short code example:
    
    ```javascript
    function makeGreeter(greeting) {
      // 'greeting' is an outer variable for the inner function
      return function(name) {
        // This inner function is a closure.
        // It "remembers" and can access the 'greeting' variable
        // from its outer scope, even after makeGreeter has finished executing.
        console.log(greeting + ", " + name + "!");
      };
    }
    
    const sayHello = makeGreeter("Hello");
    const sayHi = makeGreeter("Hi");
    
    sayHello("Alice"); // Output: Hello, Alice!
    sayHi("Bob");     // Output: Hi, Bob!
    ```
    
    In this example:
    1.  `makeGreeter` is an outer function that takes a `greeting` argument.
    2.  It returns an inner (anonymous) function. This inner function is the closure.
    3.  When `makeGreeter("Hello")` is called, it returns a new function (`sayHello`). This `sayHello` function "remembers" that its `greeting` variable should be "Hello".
    4.  Similarly, `makeGreeter("Hi")` returns another function (`sayHi`) that "remembers" `greeting` as "Hi".
    5.  When `sayHello("Alice")` is called, even though `makeGreeter` has already finished executing, `sayHello` still has access to the `greeting` variable ("Hello") from its creation environment.
  00:02:35.329 INFO  ◉ [think]      1 steps | 529 tok | 0.0s
  00:02:35.341 INFO  Execution completed {"taskId":"01KNN6BP4TPSBXRYY9130G338V","success":true,"tokensUsed":529,"cost":0.00024585,"duration":2607}
  00:02:35.341 INFO  ◉ [complete]   ✓ 01KNN6BP4TPSBXRYY9130G338V | 529 tok | $0.0002 | 2.6s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:32.734 INFO  Execution started {"taskId":"01KNN6BP4TPSBXRYY9130G338V","agentId":"test-output--explanation-with-examples-1775606552682"}
  00:02:32.738 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 4ms
  00:02:32.739 INFO  ◉ [strategy]   reactive
  00:02:35.327 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    You are a reasoning agent. Think step by step and use available tools when needed.
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    No tools available for this task.
    
    Task: Explain what a closure is in JavaScript with a short code example.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    ── thread (1 msg) ──
    [USER] Explain what a closure is in JavaScript with a short code example.
    ── raw response ──
    A closure in JavaScript is a function that remembers its outer variables and can access them even after the outer function has finished executing. This means a closure "closes over" the environment in which it was created.
    
    Here's a short code example:
    
    ```javascript
    function makeGreeter(greeting) {
      // 'greeting' is an outer variable for the inner function
      return function(name) {
        // This inner function is a closure.
        // It "remembers" and can access the 'greeting' variable
        // from its outer scope, even after makeGreeter has finished executing.
        console.log(greeting + ", " + name + "!");
      };
    }
    
    const sayHello = makeGreeter("Hello");
    const sayHi = makeGreeter("Hi");
    
    sayHello("Alice"); // Output: Hello, Alice!
    sayHi("Bob");     // Output: Hi, Bob!
    ```
    
    In this example:
    1.  `makeGreeter` is an outer function that takes a `greeting` argument.
    2.  It returns an inner (anonymous) function. This inner function is the closure.
    3.  When `makeGreeter("Hello")` is called, it returns a new function (`sayHello`). This `sayHello` function "remembers" that its `greeting` variable should be "Hello".
    4.  Similarly, `makeGreeter("Hi")` returns another function (`sayHi`) that "remembers" `greeting` as "Hi".
    5.  When `sayHello("Alice")` is called, even though `makeGreeter` has already finished executing, `sayHello` still has access to the `greeting` variable ("Hello") from its creation environment.
  00:02:35.329 INFO  ◉ [think]      1 steps | 529 tok | 0.0s
  00:02:35.341 INFO  Execution completed {"taskId":"01KNN6BP4TPSBXRYY9130G338V","success":true,"tokensUsed":529,"cost":0.00024585,"duration":2607}
  00:02:35.341 INFO  ◉ [complete]   ✓ 01KNN6BP4TPSBXRYY9130G338V | 529 tok | $0.0002 | 2.6s

═══ Spans (9) ═══
  ✓ execution.run (2608.0ms) [9dc36546…]
    ✓ execution.phase.bootstrap (4.4ms) [9dc36546…]
      ✓ phase.bootstrap.metrics (0.0ms) [9dc36546…]
    ✓ execution.phase.strategy-select (0.8ms) [9dc36546…]
      ✓ phase.strategy-select.metrics (0.0ms) [9dc36546…]
    ✓ execution.phase.think (2589.3ms) [9dc36546…]
      ✓ phase.think.metrics (0.0ms) [9dc36546…]
    ✓ execution.phase.complete (0.9ms) [9dc36546…]
      ✓ phase.complete.metrics (0.0ms) [9dc36546…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ────────────────────────────╮
│ Status:   Success   Duration: 2.6s   Steps: 1       │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 529 │
│ Cost:     ~$0.001                                   │
╰─────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]            2ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]               2.6s (1 iter, 100% of time)
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✓ 2.6s (1 iters, 529 tok)
  ⊙ [subagent    ] Static sub-agent: delegation                  ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:35.400 INFO  Execution started {"taskId":"01KNN6BRR4S1NS1ANG1T55N8CP","agentId":"test-static-sub-agent--delegation-1775606555344"}
  00:02:35.414 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 14ms
  00:02:35.415 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute, research-assistant
  00:02:38.257 INFO  ◉ [classify]   required: research-assistant
  00:02:38.257 INFO  ◉ [classify]   relevant: web-search
  00:02:39.104 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - research-assistant({"input": "object (optional)"}) — Agent: research-assistant
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: Use your research assistant to explain what a linked list is. Provide their answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    5. ⭐ REQUIRED tools MUST be called before giving FINAL ANSWER.
    ── thread (1 msg) ──
    [USER] Use your research assistant to explain what a linked list is. Provide their answer.
  00:02:39.105 DEBUG   ┄ [action]   {"tool":"research-assistant","input":"{\"input\":{\"query\":\"explain what a linked list is\"}}"}

  [36m┌─ [sub-agent: research-assistant][0m → "{"input":{"query":"explain what a linked list is"}}"
  [36m└─ [sub-agent: research-assistant][0m [32m✓[0m done | 17415 tok | 8.5s

  00:02:47.619 DEBUG   ┄ [obs]      ✓ Sub-agent "research-assistant" (17415 tok):
⚠️ You have already called web-search 8 times. Stop repeating this tool. Use final-answer to respond now. [Already done — do NOT repeat. Give FINAL ANSWER if all steps are complete.]
  00:02:57.293 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    5. ⭐ REQUIRED tools MUST be called before giving FINAL ANSWER.
    ── thread (4 msg) ──
    [USER] Use your research assistant to explain what a linked list is. Provide their answer.
        ────
        [ASSISTANT] 
        ────
        [TOOL] ✓ Sub-agent "research-assistant" (17415 tok):
    ⚠️ You have already called web-search 8 times. Stop repeating this tool. Use final-answer to respond now. [Already done — do NOT repeat. Give FINAL ANSWER if all steps are complete.]
        ────
        [USER] Completed: research-assistant ✓
    All required tools have been called. Provide your final answer now that satisfies the original task requirements.
    ── raw response ──
    It appears there was a misunderstanding in the previous turn. The output from the `research-assistant` tool was a meta-message indicating its completion and not the actual explanation of a linked list. I need to re-run the `research-assistant` to get the actual explanation.
    
    
  00:02:57.296 INFO  ◉ [think]      4 steps | 5,693 tok | 0.0s
  00:02:57.307 INFO  ◉ [act]        research-assistant (1 tools)
  00:02:59.045 INFO  Execution completed {"taskId":"01KNN6BRR4S1NS1ANG1T55N8CP","success":true,"tokensUsed":5693,"cost":0.0008908499999999999,"duration":23645}
  00:02:59.046 INFO  ◉ [complete]   ✓ 01KNN6BRR4S1NS1ANG1T55N8CP | 5,693 tok | $0.0009 | 23.6s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (13) ═══
  00:02:35.400 INFO  Execution started {"taskId":"01KNN6BRR4S1NS1ANG1T55N8CP","agentId":"test-static-sub-agent--delegation-1775606555344"}
  00:02:35.414 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 14ms
  00:02:35.415 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute, research-assistant
  00:02:38.257 INFO  ◉ [classify]   required: research-assistant
  00:02:38.257 INFO  ◉ [classify]   relevant: web-search
  00:02:39.104 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    Environment:
    Date: Tuesday, April 7, 2026
    Time: 08:02 PM
    Timezone: America/New_York
    Platform: linux (x64)
    
    Available Tools:
    - web-search({"query": "string (required)", "maxResults": "number (optional)"}) — Search the web and return a list of relevant results. Use for current information, facts, prices, news, documentation, or anything requiring up-to-date knowledge. Returns an array of results, each with { title, url, content } fields. Read the 'content' field of results to extract the information you need.
    - http-get({"url": "string (required)", "headers": "object (optional)"}) — Fetch content from a specific URL via HTTP GET. Use when you have an exact URL to retrieve (API endpoint, direct link, web page). HTML pages are automatically stripped to plain text so you can read them directly. JSON responses are parsed into objects. Returns the page text content (status prefix on error). For large results, the text is stored automatically — use recall(key, full: true) to retrieve everything. Tip: use | transform: to extract a specific field, e.g. http-get(url) | transform: result.slice(0, 2000)
    - file-read({"path": "string (required)", "encoding": "string (optional)"}) — Read a file and return its full text content as a string. Use this to read existing files or to verify what was written. Returns the raw text content on success. Fails with an error if the file does not exist.
    - file-write({"path": "string (required)", "content": "string (required)", "encoding": "string (optional)"}) — Write text to a file, creating it if it does not exist (overwrites any existing content). Returns { written: true, path: '...' } on success — once you see this, the file is saved. IMPORTANT: the required parameters are 'path' and 'content' — do NOT use 'file', 'filename', or 'filepath'.
    - code-execute({"code": "string (required)", "language": "string (optional)"}) — Execute JavaScript code in an isolated Bun subprocess and return the result. Best for: math, string transforms, JSON parsing, sorting, regex extraction, data processing. IMPORTANT: The code runs in a separate process with NO access to stored results, tool outputs, or agent state — variables like _tool_result_N do NOT exist in the code environment. To process stored data, first retrieve it with recall(key, full: true), then inline the text in code. ENVIRONMENT LIMITS: No DOMParser, no fetch, no require() for npm packages, no browser APIs. Available: Bun globals, built-in Node.js modules (Buffer, URL, crypto), String/Array/JSON methods. For HTML text already retrieved: use regex or string methods — NOT DOMParser. Example: const text = htmlString.replace(/<[^>]+>/g, ' ').replace(/\s+/g, ' ').trim(); Use console.log() to produce output. The last expression is NOT auto-returned. Returns { executed: true, result, output, exitCode } on success.
    - research-assistant({"input": "object (optional)"}) — Agent: research-assistant
    - brief({"section": "string (optional)"}) — Your environment at a glance. Call with no args for a compact overview: available tools, indexed documents, loaded skills, memory stats, recall index, context pressure, and entropy signal grade. Drill deeper with section — 'tools': full tool schemas and usage hints; 'documents': indexed sources with chunk counts; 'skills': loaded skills with one-line purposes; 'memory': semantic and episodic memory details; 'recall': all stored entries with previews; 'signal': entropy grade (A-F), trajectory, controller decisions; 'all': everything expanded. Start any complex or unfamiliar task with brief() to understand what you have available.
    - pulse({"question": "string (optional)"}) — Self-diagnostics for your current execution. Returns: signal (entropy grade A-F, trajectory shape — converging/flat/diverging/oscillating), behavior (loop detection score, tool success rate, repeated actions), context (iterations remaining, token pressure level), and a concrete recommendation based on all signals. Ask a focused question for targeted insight: pulse('am I ready to answer?') checks all final-answer requirements and lists exact blockers; pulse('should I change approach?') diagnoses stalls and loops; pulse('how much context do I have left?') checks token pressure. Call whenever you feel stuck, are about to repeat yourself, or before calling final-answer.
    
    Task: Use your research assistant to explain what a linked list is. Provide their answer.
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    5. ⭐ REQUIRED tools MUST be called before giving FINAL ANSWER.
    ── thread (1 msg) ──
    [USER] Use your research assistant to explain what a linked list is. Provide their answer.
  00:02:39.105 DEBUG   ┄ [action]   {"tool":"research-assistant","input":"{\"input\":{\"query\":\"explain what a linked list is\"}}"}
  00:02:47.619 DEBUG   ┄ [obs]      ✓ Sub-agent "research-assistant" (17415 tok):
⚠️ You have already called web-search 8 times. Stop repeating this tool. Use final-answer to respond now. [Already done — do NOT repeat. Give FINAL ANSWER if all steps are complete.]
  00:02:57.293 DEBUG   ┄ [model-io:reactive:main]
    ── system ──
    # Conductor's Workflow
    
    You are a reactive agent with four meta-tools. Use them to orient, gather, self-check, and remember.
    
    ## Before Starting (complex tasks)
    1. Call `brief()` — see your tools, documents, skills, recall index, context budget, and signal grade.
    2. If signal grade is C or below at any point, call `pulse()` to understand why.
    3. Use `find(query)` instead of choosing between rag-search and web-search — it routes automatically.
    
    ## During Execution
    - `find(query)` — gather information from any source. Specify scope only if you need to.
    - `recall(key, content)` — store your own notes, plans, and intermediate findings across steps.
    - `recall(key)` — retrieve a stored note. Default is a compact preview; add full: true for complete content.
    - `recall(query=...)` — keyword search across all stored notes when you forget key names.
    - `pulse()` — take your own pulse when stuck, unsure, or about to repeat yourself.
    
    ## Before Answering
    - If uncertain whether you're ready, call `pulse("am I ready to answer?")`.
    - The `readyToAnswer` field and `blockers` list tell you exactly what final-answer needs.
    
    ## Key Patterns
    - Same tool called 3+ times with no progress → `pulse()` to diagnose.
    - Want to preserve a finding for later → `recall(key, content)` to store it.
    - Complex new task → `brief()` first.
    - Unsure which source to search → `find(query)` with default scope, it decides for you.
    
    
    
    
    RULES:
    1. ONE action per turn. Wait for the result before proceeding.
    2. Use EXACT parameter names from the tool reference.
    3. Do NOT fabricate data. Only use information from tool results.
    4. Once a tool succeeds, do NOT repeat it.
    5. ⭐ REQUIRED tools MUST be called before giving FINAL ANSWER.
    ── thread (4 msg) ──
    [USER] Use your research assistant to explain what a linked list is. Provide their answer.
        ────
        [ASSISTANT] 
        ────
        [TOOL] ✓ Sub-agent "research-assistant" (17415 tok):
    ⚠️ You have already called web-search 8 times. Stop repeating this tool. Use final-answer to respond now. [Already done — do NOT repeat. Give FINAL ANSWER if all steps are complete.]
        ────
        [USER] Completed: research-assistant ✓
    All required tools have been called. Provide your final answer now that satisfies the original task requirements.
    ── raw response ──
    It appears there was a misunderstanding in the previous turn. The output from the `research-assistant` tool was a meta-message indicating its completion and not the actual explanation of a linked list. I need to re-run the `research-assistant` to get the actual explanation.
    
    
  00:02:57.296 INFO  ◉ [think]      4 steps | 5,693 tok | 0.0s
  00:02:57.307 INFO  ◉ [act]        research-assistant (1 tools)
  00:02:59.045 INFO  Execution completed {"taskId":"01KNN6BRR4S1NS1ANG1T55N8CP","success":true,"tokensUsed":5693,"cost":0.0008908499999999999,"duration":23645}
  00:02:59.046 INFO  ◉ [complete]   ✓ 01KNN6BRR4S1NS1ANG1T55N8CP | 5,693 tok | $0.0009 | 23.6s

═══ Spans (15) ═══
  ✓ execution.run (23646.4ms) [d1102c6e…]
    ✓ execution.phase.bootstrap (13.6ms) [d1102c6e…]
      ✓ phase.bootstrap.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.strategy-select (0.8ms) [d1102c6e…]
      ✓ phase.strategy-select.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.think (19038.8ms) [d1102c6e…]
      ✓ phase.think.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.act (1.2ms) [d1102c6e…]
      ✓ phase.act.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.observe (0.7ms) [d1102c6e…]
      ✓ phase.observe.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.memory-flush (1735.8ms) [d1102c6e…]
      ✓ phase.memory-flush.metrics (0.0ms) [d1102c6e…]
    ✓ execution.phase.complete (0.7ms) [d1102c6e…]
      ✓ phase.complete.metrics (0.0ms) [d1102c6e…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────────╮
│ Status:   Success   Duration: 23.6s   Steps: 4        │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 5,693 │
│ Cost:     ~$0.009                                     │
╰───────────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           13ms
├─ ✅  [strategy-select]      1ms
├─ ⚠️  [think]              19.0s (4 iter, 92% of time)
├─ ✅  [act]                  1ms (1 tools)
├─ ✅  [observe]              0ms
├─ ✅  [memory-flush]        1.7s
└─ ✅  [complete]             1ms

🔧 Tool Execution (1 called)
└─ ✅  research-assistant  1 calls, 8.5s avg

🧠 Reasoning Signal
├─ Grade: B   Signal: flat   Mean: 0.256   Delta: +0.425
├─ Model stalled — entropy didn't decrease across iterations
├─  iter  0 ███░░░░░░░░░░░░░░░░░ 0.150 →
├─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
├─  iter  2 ███░░░░░░░░░░░░░░░░░ 0.150 →
└─  iter  4 ███████████░░░░░░░░░ 0.575 →
   ┈┈┈
└─ 💡 Consider enabling strategy switching (.withReasoning({ enableStrategySwitching: true }))

⚠️  Alerts & Insights
└─ ⚠️  think phase blocked ≥10s (LLM latency)
✓ 23.7s (4 iters, 5693 tok)
  ⊙ [subagent    ] Dynamic sub-agent: spawn and use              ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
  00:02:59.113 INFO  Execution started {"taskId":"01KNN6CFX15YG59DDYVMFHNF2B","agentId":"test-dynamic-sub-agent--spawn-and-use-1775606579051"}
  00:02:59.129 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 17ms
  00:02:59.131 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute, spawn-agent
  00:03:01.899 INFO  ◉ [classify]   required: spawn-agent
  00:03:02.001 INFO  ◉ [think]      0 steps | 0 tok | 0.0s
  00:03:02.014 INFO  Execution completed {"taskId":"01KNN6CFX15YG59DDYVMFHNF2B","success":false,"tokensUsed":0,"cost":0,"duration":2902}
  00:03:02.014 INFO  ◉ [complete]   ✓ 01KNN6CFX15YG59DDYVMFHNF2B | 0 tok | $0.0000 | 2.9s
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }

═══ Logs (7) ═══
  00:02:59.113 INFO  Execution started {"taskId":"01KNN6CFX15YG59DDYVMFHNF2B","agentId":"test-dynamic-sub-agent--spawn-and-use-1775606579051"}
  00:02:59.129 INFO  ◉ [bootstrap]  2 semantic lines, 0 episodic | 17ms
  00:02:59.131 INFO  ◉ [strategy]   reactive | tools: web-search, http-get, file-read, file-write, code-execute, spawn-agent
  00:03:01.899 INFO  ◉ [classify]   required: spawn-agent
  00:03:02.001 INFO  ◉ [think]      0 steps | 0 tok | 0.0s
  00:03:02.014 INFO  Execution completed {"taskId":"01KNN6CFX15YG59DDYVMFHNF2B","success":false,"tokensUsed":0,"cost":0,"duration":2902}
  00:03:02.014 INFO  ◉ [complete]   ✓ 01KNN6CFX15YG59DDYVMFHNF2B | 0 tok | $0.0000 | 2.9s

═══ Spans (9) ═══
  ✓ execution.run (2903.1ms) [2c15a41d…]
    ✓ execution.phase.bootstrap (16.1ms) [2c15a41d…]
      ✓ phase.bootstrap.metrics (0.0ms) [2c15a41d…]
    ✓ execution.phase.strategy-select (0.9ms) [2c15a41d…]
      ✓ phase.strategy-select.metrics (0.0ms) [2c15a41d…]
    ✓ execution.phase.think (101.9ms) [2c15a41d…]
      ✓ phase.think.metrics (0.0ms) [2c15a41d…]
    ✓ execution.phase.complete (0.9ms) [2c15a41d…]
      ✓ phase.complete.metrics (0.0ms) [2c15a41d…]

═══ Metrics Summary ═══
╭ Agent Execution Summary ──────────────────────────╮
│ Status:   Success   Duration: 2.9s   Steps: 0     │
│ Model:    gemini-2.5-flash   (gemini)   Tokens: 0 │
│ Cost:     ~$0.000                                 │
╰───────────────────────────────────────────────────╯

📊 Execution Timeline
├─ ✅  [bootstrap]           16ms
├─ ✅  [strategy-select]      1ms
├─ ✅  [think]              102ms
└─ ✅  [complete]             1ms

🧠 Reasoning Signal
├─ Grade: A   Signal: flat   Mean: 0.150   Delta: 0.000
├─ Solved in one pass — no trajectory to analyze
└─  iter  1 ███░░░░░░░░░░░░░░░░░ 0.150 →
✗ 2.9s (0 iters, 0 tok)
    ⚠  MISSING EXPECTED: /120/ not found in output
    ⚠  result.success is FALSE

┌── COMPOSITION TESTS ──────────────────────────────────────────────────────┐
│  ⊙ pipe: sequential pipeline                      ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }
✓ 0.6s
│  ⊙ parallel: concurrent agents                    ✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
✓ Provider: gemini | Model: gemini-2.5-flash | API key: AIzaSyB6...***
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }
ℹ Reactive Intelligence telemetry enabled — anonymous entropy data helps improve the framework. Disable with { telemetry: false }
✓ 0.8s
└───────────────────────────────────────────────────────────────────────────┘


╔══════════════════════════════════════════════════════════════════════════════════╗
║                    REACTIVE AGENTS — QUALITY & EFFICIENCY REPORT                ║
╠══════════════════════════════════════════════════════════════════════════════════╣
║  Provider : gemini                                                            ║
║  Model    : gemini-2.5-flash                                                  ║
║  Tests    : 35                                                                ║
║  Date     : 2026-04-08T00:03:03.479Z                                          ║
╚══════════════════════════════════════════════════════════════════════════════════╝

┌── EFFICIENCY (5/5 passed) ──────────────────────────────────────────────────┐
│ ✅ Simple math: 2+2                            1 iters      154 tok     1.1s  $0.0000 [end_turn]
│ ✅ Simple factual: capital of France           1 iters      154 tok    704ms  $0.0000 [end_turn]
│ ✅ Simple factual: no reasoning overhead       2 iters      101 tok    697ms  $0.0000 [end_turn]
│ ✅ Direct answer: one-word response            1 iters      153 tok     2.9s  $0.0000 [end_turn]
│ ✅ Short explanation                           1 iters      196 tok    932ms  $0.0000 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── ACCURACY (4/4 passed) ────────────────────────────────────────────────────┐
│ ✅ Math reasoning: word problem                1 iters      240 tok     1.1s  $0.0001 [end_turn]
│ ✅ Logic: syllogism                            1 iters      199 tok     1.3s  $0.0000 [end_turn]
│ ✅ Code generation: fizzbuzz                   1 iters      340 tok     1.4s  $0.0001 [end_turn]
│ ✅ Factual accuracy: no hallucination          1 iters      165 tok    769ms  $0.0000 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── REASONING (3/3 passed) ───────────────────────────────────────────────────┐
│ ✅ ReAct: multi-step analysis                  1 iters    1,739 tok    10.1s  $0.0010 [end_turn]
│ ✅ Plan-Execute: structured task               4 iters    8,549 tok    35.2s  $0.0031 [end_turn]
│ ✅ Adaptive: let framework choose              2 iters      671 tok     5.6s  $0.0003 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── TOOLS (0/1 passed) ───────────────────────────────────────────────────────┐
│ ❌ Recall tool usage                           4 iters    5,254 tok    11.6s  $0.0008 [end_turn]
│    ⚠  MISSING EXPECTED: /paris/ not found in output
│    ⚠  MISSING EXPECTED: /capital/ not found in output
└───────────────────────────────────────────────────────────────────────────────┘

┌── INTELLIGENCE (3/3 passed) ────────────────────────────────────────────────┐
│ ✅ Intelligence: simple task early-stop        1 iters      162 tok    706ms  $0.0000 [end_turn]
│ ✅ Intelligence: moderate task                 1 iters    1,510 tok     8.2s  $0.0008 [end_turn]
│ ✅ Intelligence: with memory + debrief         1 iters    1,128 tok     7.9s  $0.0006 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── ROBUSTNESS (5/5 passed) ──────────────────────────────────────────────────┐
│ ✅ Empty-ish input handling                    1 iters      144 tok    679ms  $0.0000 [end_turn]
│ ✅ Instruction following: format constraint    1 iters      193 tok    870ms  $0.0000 [end_turn]
│ ✅ Multi-part question                         1 iters      177 tok     1.0s  $0.0000 [end_turn]
│ ✅ Code with explanation                       1 iters      233 tok     1.0s  $0.0001 [end_turn]
│ ✅ Ambiguous request: graceful handling        1 iters      611 tok     3.1s  $0.0003 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── CONVERGENCE (4/4 passed) ─────────────────────────────────────────────────┐
│ ✅ Converge: simple math should not loop       1 iters      162 tok    676ms  $0.0000 [end_turn]
│ ✅ Converge: list task should terminate        1 iters      180 tok    711ms  $0.0000 [end_turn]
│ ✅ Converge: opinion question                  1 iters      203 tok    762ms  $0.0000 [end_turn]
│ ✅ Converge: no-tool task with tools enabled   1 iters    3,070 tok     2.6s  $0.0005 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── STRATEGY (3/3 passed) ────────────────────────────────────────────────────┐
│ ✅ ReAct: concise factual answer               1 iters      176 tok    581ms  $0.0000 [end_turn]
│ ✅ Plan-Execute: multi-step synthesis          4 iters    3,273 tok    13.7s  $0.0008 [end_turn]
│ ✅ Adaptive: picks efficient path              2 iters      261 tok     1.2s  $0.0001 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── OUTPUT (3/3 passed) ──────────────────────────────────────────────────────┐
│ ✅ Output: code must be complete (not truncated)  1 iters      385 tok     2.0s  $0.0002 [end_turn]
│ ✅ Output: structured data must be complete    1 iters      230 tok    992ms  $0.0001 [end_turn]
│ ✅ Output: explanation with examples           1 iters      529 tok     2.6s  $0.0002 [end_turn]
└───────────────────────────────────────────────────────────────────────────────┘

┌── SUBAGENT (1/2 passed) ────────────────────────────────────────────────────┐
│ ✅ Static sub-agent: delegation                4 iters    5,693 tok    23.7s  $0.0009 [end_turn]
│ ❌ Dynamic sub-agent: spawn and use            0 iters        0 tok     2.9s  $0.0000 [end_turn]
│    ⚠  MISSING EXPECTED: /120/ not found in output
│    ⚠  result.success is FALSE
└───────────────────────────────────────────────────────────────────────────────┘

┌── COMPOSITION (2/2 passed) ─────────────────────────────────────────────────┐
│ ✅ pipe: sequential pipeline                   1 iters      166 tok    643ms  $0.0000
│ ✅ parallel: concurrent agents                 2 iters      320 tok    817ms  $0.0000
└───────────────────────────────────────────────────────────────────────────────┘

╔══════════════════════════════════════════════════════════════════════════════════╗
║                                    SUMMARY                                     ║
╠══════════════════════════════════════════════════════════════════════════════════╣
║  Pass Rate         : 33/35 (94%)                                             ║
║  Total Iterations  : 50                                                      ║
║  Total Tokens      : 36,721                                                  ║
║  Total Cost        : $0.0104                                                 ║
║  Total Duration    : 150.7                                                  s║
║  Avg Iters/Task    : 1.4                                                     ║
║  Avg Tokens/Task   : 1,049                                                   ║
║  Avg Cost/Task     : $0.0003                                                 ║
╠══════════════════════════════════════════════════════════════════════════════════╣
║  HEALTH SIGNALS                                                                ║
╠══════════════════════════════════════════════════════════════════════════════════╣
║  Iteration Explosions : 0                                                    ║
║  Hallucinations       : 0                                                    ║
║  Crashes              : 0                                                    ║
║  Max Iteration Hits   : 0                                                    ║
╚══════════════════════════════════════════════════════════════════════════════════╝

┌── EFFICIENCY GRADES ──────────────────────────────────────────────────────────┐
│  efficiency      : A+   (100% pass, avg 1.2 iters, avg 152 tokens)
│  accuracy        : A+   (100% pass, avg 1.0 iters, avg 236 tokens)
│  reasoning       : A+   (100% pass, avg 2.3 iters, avg 3653 tokens)
│  tools           : D    (0% pass, avg 4.0 iters, avg 5254 tokens)
│  intelligence    : A+   (100% pass, avg 1.0 iters, avg 933 tokens)
│  robustness      : A+   (100% pass, avg 1.0 iters, avg 272 tokens)
│  convergence     : A+   (100% pass, avg 1.0 iters, avg 904 tokens)
│  strategy        : A+   (100% pass, avg 2.3 iters, avg 1237 tokens)
│  output          : A+   (100% pass, avg 1.0 iters, avg 381 tokens)
│  subagent        : C    (50% pass, avg 2.0 iters, avg 2847 tokens)
│  composition     : A+   (100% pass, avg 1.5 iters, avg 243 tokens)
└───────────────────────────────────────────────────────────────────────────────┘

┌── RECOMMENDATIONS ────────────────────────────────────────────────────────────┐
│  ✅ All health signals clean — ready for benchmarks!
└───────────────────────────────────────────────────────────────────────────────┘

📄 Full results saved to ./quality-test-results.json
