# Phase 5: Advanced AI & Agents - Detailed Implementation (Weeks 9-10, 80 hours)

## Overview

Multi-agent orchestration, GraphRAG, and fine-tuning for production AI.

## Week 1: Agent Orchestration (40 hours)

### Day 1-2: Multi-Agent Coordination (16 hours)

**Tasks:**
1. Create `backend/internal/agents/orchestrator.go`:
   - Agent registry
   - Task distribution
   - Result aggregation
   - Error handling

2. Implement agent types:
   - Analyzer agent (requirement analysis)
   - Linker agent (relationship discovery)
   - Tester agent (test generation)
   - Reviewer agent (quality review)

3. Add coordination patterns:
   - Sequential execution
   - Parallel execution
   - Conditional execution
   - Loop execution

### Day 3-4: Task Distribution (16 hours)

**Tasks:**
1. Create `backend/internal/agents/task_queue.go`:
   - Task queuing
   - Priority handling
   - Retry logic
   - Timeout handling

2. Implement task types:
   - AnalyzeRequirement
   - DiscoverLinks
   - GenerateTests
   - ReviewQuality

3. Add performance optimization:
   - Load balancing
   - Resource allocation
   - Caching results

### Day 5: Testing & Validation (8 hours)

**Tasks:**
1. Write orchestrator tests
2. Write task queue tests
3. Test coordination patterns
4. Test error handling
5. Performance testing

## Week 2: GraphRAG & Fine-Tuning (40 hours)

### Day 1-2: GraphRAG Implementation (16 hours)

**Tasks:**
1. Create `backend/internal/rag/graph_rag.go`:
   - Graph-based context retrieval
   - Relationship analysis
   - Impact analysis
   - Dependency resolution

2. Implement graph queries:
   - Find related requirements
   - Find impacted items
   - Find dependencies
   - Find recommendations

3. Add ranking:
   - Relevance ranking
   - Impact ranking
   - Confidence scoring

### Day 3-4: Fine-Tuning (16 hours)

**Tasks:**
1. Create `backend/internal/ai/fine_tuning.go`:
   - Collect training data
   - Prepare datasets
   - Fine-tune Claude model
   - Evaluate performance

2. Implement evaluation:
   - Accuracy metrics
   - Precision/recall
   - F1 score
   - Custom metrics

3. Add deployment:
   - Model versioning
   - Gradual rollout
   - Performance monitoring

### Day 5: Integration & Testing (8 hours)

**Tasks:**
1. Integrate GraphRAG with RAG service
2. Integrate fine-tuned model
3. Write integration tests
4. Test accuracy
5. Performance testing

## Implementation Details

### Agent Orchestrator

```go
type Agent interface {
    Name() string
    Execute(ctx context.Context, task Task) (Result, error)
}

type Orchestrator struct {
    agents map[string]Agent
    queue  *TaskQueue
}

func (o *Orchestrator) Execute(ctx context.Context, workflow Workflow) ([]Result, error) {
    // Distribute tasks
    // Aggregate results
    // Handle errors
}
```

### GraphRAG Query

```go
type GraphRAGQuery struct {
    ItemID      string
    QueryType   string // "related", "impacted", "dependencies"
    Depth       int
    Limit       int
}

func (q *GraphRAGQuery) Execute(ctx context.Context, graph *neo4j.Driver) ([]*Item, error) {
    // Query graph
    // Rank results
    // Return top items
}
```

### Fine-Tuning Configuration

```go
type FineTuningConfig struct {
    Model           string // "claude-3.5-sonnet"
    TrainingData    []TrainingExample
    ValidationData  []TrainingExample
    Epochs          int
    LearningRate    float64
}

type TrainingExample struct {
    Input  string
    Output string
}
```

## Success Criteria

✅ Agent orchestration working
✅ Task distribution working
✅ GraphRAG working
✅ Fine-tuning working
✅ Accuracy >90%
✅ All tests passing

## Expected Results

- **Accuracy:** 90%+ on requirement analysis
- **Performance:** <2s for complex queries
- **Scalability:** 100+ concurrent agents
- **Quality:** Custom model for domain

## Troubleshooting

**Issue:** Agent coordination failing
- Check task distribution
- Check error handling
- Review coordination logic

**Issue:** GraphRAG slow
- Check graph indexes
- Check query optimization
- Review ranking logic

**Issue:** Fine-tuning not improving
- Check training data quality
- Check hyperparameters
- Review evaluation metrics

## Next Phase

After Phase 5 complete:
- Move to Phase 6: Performance & Optimization
- Optimize database
- Optimize API
- Add FinOps monitoring

