Continuous Evaluation & Reinforcement Pipeline

⚙️ Continuous Evaluation & Reinforcement

Goal: Integrate deepeval and ragas as automated performance gates with feedback loop.

Integration Components

1. Performance Gates

Integrate into compliance-engine:

• deepeval - LLM evaluation framework
• ragas - RAG assessment
• Automated scoring
• Quality thresholds
• Regression detection

2. Data Collection Pipeline

agent-ops (executes) → langfuse (logs) → compliance-engine (scores)
                                              ↓
                                        Qdrant (stores)
                                              ↓
                                    Buildkit (optimizes)

3. Reward Signal Aggregation

• Track inference data in langfuse
• Calculate performance metrics
• Generate reward signals
• Feed back to buildkit

Feedback Loop

1. agent-ops executes workload
2. compliance-engine scores it (deepeval/ragas)
3. Results stored in qdrant (vector memory)
4. Buildkit retrieves metrics to optimize next deploy

Automated Performance Gates

• Quality thresholds per agent type
• Regression detection
• A/B test validation
• Progressive rollout gates
• Rollback triggers

Metrics Tracked

• Answer relevance
• Context precision/recall
• Faithfulness
• Response latency
• Token efficiency
• User satisfaction
• Error rates

Implementation Tasks

• Integrate deepeval into compliance-engine
• Add ragas for RAG evaluation
• Build langfuse data pipeline
• Create scoring automation
• Implement reward signal calculation
• Add performance dashboards
• Build feedback loop to buildkit

Expected Result

Autonomous improvement pipeline using existing pieces.

Priority

High - Core quality automation

Phase

Phase 2 - Reinforcement loop