2026-04-07

# How to Debug AI Agent Decision Trees: A Practical Guide

Debugging AI agents requires a fundamentally different approach compared to traditional software. When your web application fails, you can step through code, inspect variables, and follow the execution path. But AI agents operate on different principles—they make probabilistic decisions, use external tools, and reason through natural language. Traditional debuggers fall short when faced with non-deterministic, multi-step reasoning chains.

This guide introduces trace-based debugging as a systematic solution for understanding and debugging AI agent behavior.

## Why AI Agents Break Traditional Debuggers

Traditional debugging relies on deterministic execution:

- Breakpoints pause execution at specific locations
- Variable inspectors show state at exact moments
- Call stacks show the precise sequence of function calls

AI agents shatter these assumptions:

    # Traditional debugging fails here because:
    # 1. LLM responses are non-deterministic
    # 2. Tool calls depend on probabilistic reasoning
    # 3. The "decision" happens in the model's weights, not code
    async def agent_function(user_input: str):
        # Where does the "debugging" happen?
        llm_response = await llm_call(f"Process: {user_input}")  # Black box
        tool_calls = parse_tool_calls(llm_response)              # Heuristic parsing
        result = await execute_tools(tool_calls)                  # Network calls
        return result

When something goes wrong, you're left with:

- Print statements showing partial information
- Log files scattered across different systems
- Manual reconstruction of what happened
- No way to replay or test hypotheses

## The Problem with Traditional Approaches

### Print Statements and Logging

    # The "spray and pray" approach
    print(f"[AGENT] Processing: {user_input}")
    print(f"[LLM] Response: {llm_response}")
    print(f"[TOOLS] Calling: {tool_calls}")
    print(f"[RESULT] Output: {result}")

Limitations:

- No causal relationships between events
- Hard to trace complex decision paths
- Information overload vs. underload
- No way to visualize the decision tree

### Existing Observability Tools

Tools like LangSmith and Weights & Biases help, but they:

- Focus on LLM calls, not agent reasoning
- Often require complex instrumentation
- May send sensitive data to cloud services
- Don't always capture the full decision context

## Introducing Trace-Based Debugging

Trace-based debugging captures the full causal chain of agent execution. Every decision, tool call, and LLM interaction becomes an event in a structured timeline.

### The Core Concept

Instead of debugging through code, you debug through events. Each agent action generates a traceable event with:

- Reasoning: Why this action was chosen
- Confidence: How certain the agent was
- Evidence: What information led to this decision
- Provenance: Parent-child relationships between actions

### Getting Started with Peaky Peek

The simplest way to begin is with the `@trace` decorator:

    pip install peaky-peek-server
    peaky-peek --open  # Starts API server at http://localhost:8000

    from agent_debugger_sdk import trace, init

    init()  # Initialize local tracing

    @trace(name="weather_agent", framework="custom")
    async def weather_agent(user_query: str) -> str:
        # Your agent logic here
        if "rain" in user_query.lower():
            decision = "call_weather_api"
            confidence = 0.9
        else:
            decision = "provide_general_info"
            confidence = 0.7

        # The trace decorator automatically captures:
        # - Agent start/end
        # - LLM calls
        # - Tool calls and results
        # - Decisions and reasoning

        return await get_weather_response()

### Advanced: Manual Event Recording

For more control, use `TraceContext`:

    from agent_debugger_sdk import TraceContext, init

    init()

    async def complex_agent(user_input: str) -> str:
        async with TraceContext(agent_name="research_assistant", framework="custom") as ctx:
            # Record the initial decision
            await ctx.record_decision(
                reasoning="User wants market research",
                confidence=0.85,
                chosen_action="search_multiple_sources",
                evidence=[
                    {"type": "user_input", "content": user_input},
                    {"type": "context", "content": "Market analysis requested"}
                ]
            )

            # First tool call
            await ctx.record_tool_call("web_search", {"query": user_input})
            web_results = await perform_web_search(user_input)

            # Record tool result
            await ctx.record_tool_result(
                "web_search",
                result=web_results,
                duration_ms=1200
            )

            # Second decision
            await ctx.record_decision(
                reasoning="Web search incomplete, need financial data",
                confidence=0.75,
                chosen_action="call_financial_api",
                evidence=[
                    {"type": "tool_result", "content": web_results},
                    {"type": "analysis", "content": "Missing financial metrics"}
                ]
            )

## Visualizing Reasoning Chains

The real power comes from visualizing the decision tree. Peaky Peek's UI shows:

### Interactive Decision Tree

    ┌─────────────────────────────────────────┐
    │ Weather Agent                           │
    │ Started: 2026-04-07 10:30:15           │
    └─────────────────────────────────────────┘
        │
        ▼
    ┌─────────────────────────────────────────┐
    │ Decision: Get Weather Data              │
    │ Reasoning: User asked about weather   │
    │ Confidence: 90%                         │
    │ Evidence:                              │
    │ • "What's the weather in Seattle?"      │
    └─────────────────────────────────────────┘
        │
        ├─→ ┌─────────────────────────────────┐
        │   │ Tool Call: weather_api          │
        │   │ Arguments: {city: "Seattle"}    │
        │   └─────────────────────────────────┘
        │       │
        │       ▼
        │   ┌─────────────────────────────────┐
        │   │ Tool Result: Success             │
        │   │ Response: {temp: 52, forecast:  │
        │   │           "rain"}               │
        │   │ Duration: 1.2s                  │
        │   └─────────────────────────────────┘
        │
        └─→ ┌─────────────────────────────────┐
            │ Decision: Respond to User        │
            │ Reasoning: Weather data complete │
            │ Confidence: 95%                  │
            └─────────────────────────────────┘

### Timeline View

The timeline shows events in chronological order with rich metadata:

    10:30:15.123 [AGENT_START] weather_agent
    10:30:15.456 [DECISION] confidence=0.9 reason="User asked about weather"
    10:30:15.789 [TOOL_CALL] weather_api arguments={"city": "Seattle"}
    10:30:17.012 [TOOL_RESULT] duration_ms=1200 result={"temp": 52}
    10:30:17.345 [LLM_REQUEST] model=gpt-4o messages=[...]
    10:30:18.678 [LLM_RESPONSE] tokens=45 cost=$0.001
    10:30:18.679 [AGENT_END] success=True

## Advanced Features

### Checkpoint Replay

Ever want to debug what happened at a specific moment? Checkpoints capture agent state at critical points:

    async with TraceContext(...) as ctx:
        await ctx.record_checkpoint(
            name="before_search",
            metadata={"strategy": "web_first", "confidence": 0.8}
        )

        # Agent continues...
        # Later, you can replay from this checkpoint

### Failure Detection and Clustering

Peaky Peek automatically identifies:

- High-severity events: Errors, refusals, low-confidence decisions
- Failure clusters: Similar error patterns across sessions
- Replay value: Events most likely to help understand failures

    # Automatic failure detection
    if confidence < 0.5:
        # This gets flagged for review
        await ctx.record_event(
            event_type="low_confidence_decision",
            importance=0.9
        )

### Loop Detection

AI agents can get stuck in loops. The system automatically detects:

    # Pattern: Same request → Same response → Same request
    if loop_detected:
        await ctx.record_event(
            event_type="potential_loop",
            metadata={
                "cycle_length": 3,
                "similar_requests": 5
            }
        )

## Practical Debugging Workflow

### Step 1: Instrument Your Agent

Start with minimal instrumentation:

    @trace_agent(name="my_agent")
    async def my_agent(prompt: str):
        # Existing agent code
        pass

### Step 2: Run and Identify Issues

Execute your agent. In the Peaky Peek UI:

- Look for red events (errors)
- Check low-confidence decisions
- Examine long tool call durations

### Step 3: Drill Down with Provenance

Click on any event to see:

- Why it was chosen
- What evidence led to it
- Parent-child relationships

### Step 4: Replay from Checkpoints

Replay your agent's execution from any checkpoint:

- Step forward/backward through decisions
- Inspect agent state at each point
- Test different hypotheses

### Step 5: Analyze Patterns

Use the analytics panel to:

- Find recurring failure patterns
- Compare successful vs. failed runs
- Identify high-cost operations

## Real-World Example: Debugging a Shopping Agent

Let's debug a shopping agent that keeps failing to find products:

    @trace(name="shopping_agent")
    async def shopping_agent(user_request: str):
        # Original code - what's going wrong?
        intent = classify_intent(user_request)  # Black box
        if intent == "search":
            query = extract_search_query(user_request)
            results = search_products(query)
        else:
            results = handle_other_intent(intent)

        if not results:
            return "No products found"

        return format_results(results)

Problem: Agent keeps returning "No products found"

Debugging with traces:

- Run with Peaky Peek enabled
- Discover that `classify_intent` is returning "other" for product searches
- See the evidence:

```

[DECISION] confidence=0.3

reason="Unclassified intent"

evidence=[{"type": "user_input", "content": "find cheap laptops"}]

```

- Fix the intent classifier
- Re-run and see the improvement

## Best Practices

### 1. Capture Meaningful Decisions

Don't trace every function call. Focus on:

- High-level decisions
- Tool usage
- Error conditions
- Confidence changes

    # Good: Capture the business decision
    await ctx.record_decision(
        reasoning="User wants product recommendations",
        confidence=0.8,
        chosen_action="recommend_products",
        evidence=[...]
    )

    # Bad: Trace internal implementation details
    await ctx.record_tool_call("database_query", query="SELECT * FROM products")

### 2. Include Evidence

Record what information led to each decision:

    await ctx.record_decision(
        reasoning="User compared prices",
        confidence=0.9,
        chosen_action="show_cheapest_option",
        evidence=[
            {"type": "tool_result", "content": price_comparison},
            {"type": "user_preference", "content": "budget-conscious"}
        ]
    )

### 3. Set Confidence Levels

Be honest about uncertainty:

    if confidence < 0.5:
        # This needs review
        await ctx.record_decision(
            reasoning="Unclear user intent",
            confidence=0.4,
            chosen_action="ask_for_clarification",
            evidence=[...]
        )

### 4. Use Checkpoints Strategically

Mark important state transitions:

    async with TraceContext(...) as ctx:
        # Initial state
        await ctx.record_checkpoint("initial_analysis")

        # After key processing
        await ctx.record_checkpoint("search_complete")

        # Before final response
        await ctx.record_checkpoint("response_ready")

## Conclusion

Debugging AI agents doesn't have to be guesswork. By capturing the full decision tree with trace-based debugging, you can:

- Understand why agents make specific decisions
- Replay execution to test hypotheses
- Identify patterns in failures
- Build more reliable systems

Start with the `@trace` decorator for simple cases, and use `TraceContext` for more complex scenarios. The key is to treat your agent not as a black box, but as a system with observable, traceable behavior.

Ready to debug your agents with confidence? Try Peaky Peek today and see the difference trace-based debugging makes.