# Prompt Engineering & Delegation Improvement Review

## Current State Analysis

### Strengths
1. **Well-structured prompts**: Clear role definitions with specific responsibilities
2. **Delegation framework**: Structured delegation with `delegate_task` tool
3. **Role anchoring**: Periodic reminders to stay in role
4. **Task ledger**: Tracking of delegation outcomes
5. **Specialist agents**: Planner, Explorer, Engineer with clear boundaries

### Weaknesses Identified

#### 1. **Insufficient Thinking/Reasoning Promotion**
**Issue**: Tech Lead doesn't explicitly demonstrate thinking process
**Impact**: Missed opportunities for transparency and better decision-making
**Current State**: Tech Lead prompt emphasizes "Plan First" but doesn't require explicit thinking output

#### 2. **Delegation Underutilization**
**Issue**: Tech Lead often does work that should be delegated
**Impact**: Inefficient use of specialist capabilities
**Current State**: Delegation happens but could be more systematic

#### 3. **Lack of Structured Thinking Patterns**
**Issue**: No standardized thinking frameworks
**Impact**: Inconsistent reasoning quality
**Current State**: Ad-hoc thinking without clear methodology

#### 4. **Missing Reflection Loops**
**Issue**: No structured reflection on delegation outcomes
**Impact**: Missed learning opportunities
**Current State**: Delegation results are consumed but not systematically analyzed

## Recommended Improvements

### Phase 1: Enhanced Thinking & Reasoning Framework

#### 1.1 Implement "Think-Aloud" Protocol
**Goal**: Make Tech Lead's thinking process explicit
**Implementation**:
```python
THINK_ALOUD_PROTOCOL = """
**Thinking Protocol (REQUIRED)**:
Before taking any action, you MUST explicitly think through:
1. **Problem Analysis**: What exactly needs to be solved?
2. **Context Review**: What do we already know? (Check knowledge graph, RAG, previous work)
3. **Option Generation**: What are possible approaches?
4. **Decision Criteria**: What factors determine the best approach?
5. **Plan Formulation**: What specific steps will you take?

Format your thinking as:
```
THINKING:
1. Problem: <analysis>
2. Context: <relevant information>
3. Options: <possible approaches>
4. Decision: <chosen approach with rationale>
5. Plan: <specific steps>
```

Only after completing this thinking process should you begin execution.
"""
```

#### 1.2 Add Structured Decision Framework
**Goal**: Improve decision quality with systematic approach
**Implementation**:
```python
DECISION_FRAMEWORK = """
**Decision Framework (USE FOR COMPLEX TASKS)**:
For complex decisions, use this framework:

A. **Define Success Criteria**: What does "done" look like?
B. **Identify Constraints**: What limitations exist? (time, resources, dependencies)
C. **Evaluate Risks**: What could go wrong? How likely? How severe?
D. **Consider Alternatives**: What other approaches exist?
E. **Make Recommendation**: Which option? Why?

Format as:
```
DECISION FRAMEWORK:
A. Success: <criteria>
B. Constraints: <limitations>
C. Risks: <potential issues>
D. Alternatives: <other options>
E. Recommendation: <chosen option with rationale>
```
"""
```

### Phase 2: Enhanced Delegation Promotion

#### 2.1 Add Delegation Decision Checklist
**Goal**: Make delegation decisions more systematic
**Implementation**:
```python
DELEGATION_DECISION_CHECKLIST = """
**Delegation Decision Checklist (USE BEFORE DOING WORK)**:
Before starting work, ask yourself:

1. **Is this a specialized task?** 
   - Architecture/planning → Delegate to Planner
   - Research/exploration → Delegate to Explorer  
   - Implementation/testing → Delegate to Engineer

2. **Do I have the right expertise?**
   - If specialist exists, use them

3. **Is this parallelizable?**
   - Can this be done while I work on something else?

4. **What's the complexity?**
   - High complexity → Strong case for delegation

If ANY answer suggests delegation, USE `delegate_task` instead of doing it yourself.
"""
```

#### 2.2 Implement Delegation Scorecard
**Goal**: Track and improve delegation effectiveness
**Implementation**:
```python
DELEGATION_SCORECARD = """
**Delegation Scorecard (REVIEW AFTER EACH DELEGATION)**:
After receiving delegation results, evaluate:

✅ **Completeness**: Did they meet all requirements?
✅ **Quality**: Is the work well-done?
✅ **Efficiency**: Was it done in reasonable time?
✅ **Learning**: Did we learn something new?

Record insights in knowledge graph for future reference.
"""
```

### Phase 3: Enhanced Role Prompts

#### 3.1 Update Tech Lead Prompt
**Current Issue**: Missing explicit thinking requirements
**Recommended Changes**:

Add to `LEADER_INSTRUCTIONS`:
```python
THINKING_AND_REASONING = """
**Thinking & Reasoning Protocol**:

A. **ALWAYS THINK BEFORE ACTING**: 
   - For simple tasks: Use quick mental check (1-2 sentences)
   - For complex tasks: Use structured thinking framework

B. **THINK-ALOUD REQUIRED**: 
   Your thinking MUST be visible in your responses. Format as:
   ```
   THINKING: <your reasoning process>
   ACTION: <what you'll do based on thinking>
   ```

C. **DELEGATION-FIRST MINDSET**:
   Before doing work yourself, ALWAYS consider:
   1. Could a specialist do this better/faster?
   2. Is this within my core responsibilities?
   3. Would delegation free me for higher-value work?

D. **REFLECTION AFTER ACTION**:
   After completing work (or receiving delegation results):
   1. What worked well?
   2. What could be improved?
   3. What should we remember for next time?
```

**Example Tech Lead Response Pattern**:
```
THINKING:
1. Problem: User wants to add authentication middleware
2. Context: Current codebase uses FastAPI, has user models
3. Options: 
   - Write middleware myself
   - Delegate to Engineer for implementation
   - Ask Explorer to research best practices
4. Decision: Delegate to Engineer for implementation, ask Explorer for research
5. Plan: Use delegate_task for both specialists

ACTION:
I'll delegate implementation to Engineer and research to Explorer.
```

#### 3.2 Enhance Specialist Prompts
**Add thinking requirements to all specialists**:
```python
SPECIALIST_THINKING = """
**Specialist Thinking Protocol**:
Before starting work, think through:
1. **Understanding**: Do I fully understand the task?
2. **Approach**: What's the best way to accomplish this?
3. **Risks**: What could go wrong?
4. **Verification**: How will I know I'm done?

Format your thinking at the start of your response.
"""
```

### Phase 4: Implementation Strategy

#### Week 1: Foundation
1. Add thinking protocols to prompts
2. Update Tech Lead prompt with think-aloud requirement
3. Create test cases for thinking patterns

#### Week 2: Delegation Enhancement
4. Add delegation decision checklist
5. Implement delegation scorecard
6. Update orchestrator to track delegation metrics

#### Week 3: Integration & Testing
7. Integrate thinking frameworks with existing tools
8. Create validation tests
9. Update documentation

#### Week 4: Refinement
10. Gather feedback from usage
11. Refine prompts based on results
12. Create best practices guide

### Technical Implementation Details

#### 1. Prompt Updates
**File**: `src/henchman/agents/prompts.py`
**Changes**:
- Add `THINKING_AND_REASONING` constant
- Add `DELEGATION_DECISION_CHECKLIST`
- Update `LEADER_INSTRUCTIONS` to include thinking protocols
- Add thinking requirements to specialist prompts

#### 2. Orchestrator Enhancements
**File**: `src/henchman/agents/orchestrator.py`
**Changes**:
- Track thinking pattern usage
- Monitor delegation frequency
- Add metrics collection

#### 3. Testing Framework
**New Tests**:
- Test thinking pattern adherence
- Test delegation decision-making
- Test reflection quality

### Success Metrics

#### Quantitative
- **Delegation Rate**: Increase from current to target 40% of complex tasks
- **Thinking Visibility**: 100% of Tech Lead responses include thinking section
- **Decision Quality**: Reduce rework by 30%

#### Qualitative
- **Transparency**: Users can see reasoning process
- **Efficiency**: Better task distribution
- **Learning**: Improved knowledge capture

### Risks & Mitigations

1. **Prompt Bloat**: Keep thinking frameworks concise
2. **Performance Impact**: Minimal - thinking is text-only
3. **User Confusion**: Clear formatting and examples
4. **Over-engineering**: Start simple, iterate based on feedback

### Expected Outcomes

1. **Better Decision-Making**: Structured thinking improves quality
2. **Increased Delegation**: Systematic approach promotes specialist use
3. **Improved Transparency**: Users understand agent reasoning
4. **Enhanced Learning**: Reflection captures insights for future

### Next Steps

1. **Immediate**: Update Tech Lead prompt with thinking requirement
2. **Short-term**: Implement delegation decision checklist
3. **Medium-term**: Add thinking frameworks to specialists
4. **Long-term**: Create thinking quality metrics and feedback loops

This approach balances structure with flexibility, providing clear guidance while allowing agents to adapt to specific situations.