# Agent Job Output & Delivery Implementation Summary

**Date:** 2026-02-19  
**Status:** ✅ Phase 1 Complete (Documentation)

## What Was Built

Comprehensive documentation system for agent job outputs, delivery mechanisms, and sub-agent collaboration patterns.

### 1. Main Guide: `AGENT_JOB_OUTPUT_GUIDE.md`

**Location:** `src/resources/agent-docs/AGENT_JOB_OUTPUT_GUIDE.md`

**Contents:**
- **Output Modes** (Natural, Structured, Tool-Based, SQLite)
- **Delivery Mechanisms** (Chat, Job Record, Live Logs, Memory, SQLite)
- **Sub-Agent Delegation** (Context passing, output delivery, limitations)
- **Decision Trees** (When to use each approach)
- **Code Examples** (All patterns with working code)
- **Consumption Patterns** (How to use structured output in downstream jobs)
- **Troubleshooting** (Common issues and solutions)

**Key Sections:**
- Quick Reference Matrix
- Structured Output Consumption Pattern (Python + Agent)
- Sub-Agent Context Rules with complete examples
- Live Monitoring Sub-Agent Work
- Current Limitations & Workarounds
- Future Enhancement: Mini-Chat Card vision

---

### 2. Updated Delegation Guide: `DELEGATION_STRATEGY.md`

**Location:** `src/resources/agent-docs/DELEGATION_STRATEGY.md`

**New Sections Added:**
- **Sub-Agent Communication Patterns**
  - How sub-agents receive context
  - How results flow back to main agent
  - Passing data between sub-agents
  
- **Sub-Agent Output Delivery Options**
  - Return to main agent only
  - Deliver to chat
  - Background execution
  - No delivery (logs only)
  
- **When Sub-Agents Are NOT Appropriate**
  - Tasks requiring user clarification
  - Multi-turn user interactions
  - Real-time user approval/feedback
  - Tasks needing main conversation context
  
- **Workarounds for Current Limitations**
  - Pre-fill context pattern
  - Two-phase delegation
  - Partial result with NEED_INFO marker
  
- **Live Monitoring Sub-Agent Work**
  - DelegationCard in UI
  - Real-time logs via WebSocket
  
- **Future Enhancement: Mini-Chat Card**
  - Vision for multi-turn agent collaboration
  - User join functionality
  - 3-way conversations

---

### 3. System Prompt Update: `SystemPrompt.ts`

**Location:** `src/core/agents/SystemPrompt.ts`

**New Section:** `buildJobOutputStrategySection()`

**Contents:**
- Output Modes Decision (Natural, Structured, Tool-Based, SQLite)
- Delivery Mechanisms (Chat, Job Record, Memory)
- Sub-Agent Context Rules (Critical limitations)
- Structured Output Consumption Pattern
- Quick Decision Tree

**Integration:** Added to prompt build sequence after `buildAutomationArchitectureSection()`

---

### 4. Quick Reference Skill: `agent-job-output-strategy.md`

**Location:** `src/resources/skills/agent-job-output-strategy.md`

**Purpose:** Fast lookup for agents during task execution

**Contents:**
- When to use each output mode
- Delivery mechanisms with code
- Sub-agent context rules
- Quick decision tree
- Common patterns (Extract-Transform-Load, Research-Summarize-Chat, SQLite-App-User)

---

## Key Improvements

### 1. Structured Output Consumption

**Problem:** Agents didn't know how to consume structured output from other jobs.

**Solution:** Documented Python + Agent patterns:

```python
# Python reads agent job output
job_json = Path.home() / "Papr" / "jobs" / "agent-job-id" / "job.json"
data = json.loads(json.load(open(job_json))["lastOutput"])
```

```javascript
// Agent reads job output
const jobData = read_job_file({ jobId: "extract-job", filePath: "job.json" })
const output = JSON.parse(jobData.lastOutput)
```

### 2. Sub-Agent Context Clarity

**Problem:** Unclear what sub-agents can/cannot access.

**Solution:** Explicit rules + examples:

**Sub-agents CANNOT:**
- ❌ Access main conversation history
- ❌ Ask user questions mid-execution
- ❌ See other sub-agent results

**Sub-agents ONLY see:**
- ✅ `task` parameter
- ✅ `context` parameter
- ✅ `systemPrompt`
- ✅ Environment variables

**Examples:** Bad context vs Good context with complete information.

### 3. Delivery Strategy Decision Trees

**Problem:** Unclear when to use chat delivery vs job record vs SQLite.

**Solution:** Clear decision tree:

```
User-facing text? → Natural + deliver: { channel: "chat" }
Code will parse it? → Structured + downstream job reads lastOutput
Creating artifacts? → Tool-based (write_file, create_app)
UI needs to query? → SQLite + link_app_data_source
Needs specialization? → delegate_task with complete context
```

### 4. Future Enhancement: Mini-Chat Card

**Vision:** Multi-turn agent-to-agent and user-sub-agent collaboration

**Features:**
- Sub-agent gets persistent chat session
- Main agent can respond and guide
- User can observe or click "Join" to participate
- Enables natural back-and-forth conversation

**Benefit:** Solves the "sub-agent can't ask questions" limitation.

---

## Files Created/Modified

### Created
1. `src/resources/agent-docs/AGENT_JOB_OUTPUT_GUIDE.md` (658 lines)
2. `src/resources/skills/agent-job-output-strategy.md` (225 lines)
3. `docs/AGENT_JOB_OUTPUT_IMPLEMENTATION.md` (this file)

### Modified
1. `src/resources/agent-docs/DELEGATION_STRATEGY.md` (+400 lines)
2. `src/core/agents/SystemPrompt.ts` (+150 lines, new section)

### Unchanged (Referenced)
1. `src/gateway/services/AgentService.ts` (runIsolatedJobSession, runStructuredJobSession)
2. `src/gateway/services/jobs/executors/AgentJobExecutor.ts` (output modes)
3. `src/gateway/services/SubAgentService.ts` (delegation flow)
4. `ui/components/Chat/JobStatusCard.tsx` (live logs display)
5. `ui/components/Chat/DelegationCard.tsx` (sub-agent UI)

---

## Impact

### For Agents
- ✅ Clear guidance on choosing output strategies
- ✅ Know how to consume structured output from other jobs
- ✅ Understand sub-agent context limitations
- ✅ Know when to use chat delivery vs SQLite
- ✅ Can reference examples inline while working

### For Users
- ✅ More predictable job outputs
- ✅ Better structured data pipelines
- ✅ Clearer sub-agent results
- ✅ Future: Multi-turn agent collaboration (Phase 2)

### For Developers
- ✅ Comprehensive reference for output patterns
- ✅ Examples for all major use cases
- ✅ Clear architecture documentation
- ✅ Roadmap for future enhancements

---

## Testing Recommendations

### 1. Natural Output + Chat Delivery
```javascript
create_job({
  name: "Test Research",
  type: "agent",
  prompt: "Research the top 3 AI frameworks",
  deliver: { channel: "chat", targetId: currentChatId }
})
run_job({ jobId: "test-research" })
```
**Expected:** Result appears as assistant message in chat.

### 2. Structured Output → Python Consumption
```javascript
create_job({
  name: "test-extract",
  type: "agent",
  outputMode: "structured",
  outputSchema: {
    type: "object",
    properties: { items: { type: "array" } }
  }
})
run_job({ jobId: "test-extract", wait: true })

create_job({
  name: "test-process",
  type: "python",
  command: "python3 main.py"
})
// main.py reads test-extract/job.json → parses lastOutput
run_job({ jobId: "test-process" })
```
**Expected:** Python job successfully reads and processes structured JSON.

### 3. Sub-Agent with Complete Context
```javascript
delegate_task({
  task: "Review ~/test/auth.js for security issues",
  context: `
    File: ~/test/auth.js
    Focus: Password hashing, session management
    Expected: Actionable recommendations
  `,
  reportChatId: currentChatId
})
```
**Expected:** 
- DelegationCard appears with live logs
- Result delivered to chat
- Main agent receives result in tool return

### 4. SQLite Output → App Query
```python
# Job writes to SQLite
db.execute("CREATE TABLE test_data (id INT, value TEXT)")
db.execute("INSERT INTO test_data VALUES (1, 'test')")
```
```javascript
link_app_data_source({ appId: "test-app", jobId: "test-job" })
// App queries: SELECT * FROM test_data
```
**Expected:** App successfully reads data from job SQLite DB.

---

## Next Steps

### Phase 2: Mini-Chat Card (Future)

**Backend:**
1. Create persistent chat sessions for sub-agents
2. Add tools: `request_agent_input`, `respond_to_sub_agent`, `join_sub_agent_chat`
3. Store delegation → chatId mapping
4. Enable multi-turn conversation flow

**Frontend:**
1. Design `MiniChatCard` component
2. Message thread display (main agent ↔ sub-agent)
3. "Join" button for user participation
4. WebSocket integration for real-time updates

**Benefits:**
- Sub-agents can ask clarifying questions
- Main agent can supervise and guide
- User can step in when needed
- True agent-to-agent collaboration

---

## Documentation Quality

**Comprehensive:** 658 lines of main guide + 400 lines of delegation updates
**Examples:** 15+ working code examples across all patterns
**Decision Support:** Multiple decision trees and quick references
**Future-Ready:** Mini-Chat Card vision documented for Phase 2

**Agent-Friendly:**
- Injected into SystemPrompt for every conversation
- Available as skill for quick lookup
- Referenced in all automation guides

---

## See Also

- `CLAUDE.md` - Project context and learnings
- `APP_AND_JOBS_GUIDE.md` - Apps and jobs architecture
- `SUBAGENT_CREATION_GUIDE.md` - Creating specialized sub-agents
- `00-START-HERE.md` - Complete tool reference
