v2.0 — Self-Learning Pipeline
These features transform the tool from a reactive code generator into a self-improving autonomous system. Each capability adds a new dimension of machine learning to the pipeline, reducing human intervention and improving output quality over time.
1. Closed-Loop Outcome Scoring
Foundational LayerTechnical Architecture
- Entity:
OutcomeCard— JPA entity in H2/PostgreSQL - Service:
OutcomeScoringService— generates structured outcome cards after QA - Trigger:
PRCreatedEventlistener auto-generates cards - Storage: H2 table
outcome_cardswith indexed lookups by tech stack - AI Integration: Bedrock generates "lessons learned" via meta-analysis prompt
- Retrieval: During Stage 2, top-N matching cards injected into analysis prompt
Business Impact
- Reduces analysis time — past successes/failures guide decisions instantly
- Avoids repeated failures — if an approach failed before, system warns proactively
- Quantifies ROI — tracks time-to-complete and fix iterations across all requirements
- Enables trend analysis — success rates by tech stack reveal strengths/weaknesses
- Transforms KB from "here's similar code" to "here's what worked and what didn't"
2. Prompt Evolution via Meta-Learning
Adaptive IntelligenceTechnical Architecture
- Service:
PromptEvolutionService— tracks prompt→outcome correlations - Blind Spot Detection: Categories appearing in >50% of runs flagged as ineffective rules
- Amendment Generation: Bedrock meta-prompt generates ALWAYS/NEVER rules
- Safety Gate: All amendments are STAGED for human review before activation
- Metrics: Per-template avg QA score, avg iterations, total findings tracked
- Trigger: Auto-runs after every N requirements (configurable threshold)
Business Impact
- Self-improving prompts — system gets smarter with every requirement processed
- Addresses "stale rules" — detects when prompt rules aren't preventing errors
- Reduces QA iterations — better prompts → fewer errors → less fix loop work
- Human-in-the-loop safety — amendments never go live without approval
- Measurable improvement — before/after metrics prove prompt evolution value
4. Proactive Defect Prediction
Pre-Generation IntelligenceTechnical Architecture
- Service:
DefectPredictionService— pre-generation risk analysis - AI Prompt: Bedrock predicts error categories by requirement + tech stack
- Pattern Matching: Cross-references full Error Pattern Library for weighted prevention
- Guard Code: Auto-generates defensive code snippets for HIGH-risk areas
- File Risk Scoring: Identifies historically problematic files for extra scrutiny
- Integration Point: Runs in
CodeGenerationServicebefore story loop starts
Business Impact
- Shift-left quality — catches likely defects BEFORE code is generated
- Reduces fix iterations — predicted problems are prevented at source
- Smarter resource allocation — high-risk requirements get more defensive code
- Quantifiable ROI — track defect prediction accuracy vs actual QA findings
- Cost savings — fewer LLM calls for fix loops = lower Bedrock costs
5. Autonomous Self-Resolution (Stage 2)
Human Touchpoint ReductionTechnical Architecture
- Service:
SelfResolutionService— multi-strategy question resolver - 4 Resolution Strategies: KB search, past Q&A matching, repo context inference, AI reasoning
- Confidence Scoring: Each resolution gets a 0.0–1.0 confidence; threshold = 0.7
- Integration: Hooks into
ProposalService.runAnalysis()before user escalation - Outcome Cards: Uses historical outcomes for additional reasoning context
- Safety: All auto-resolutions are marked with source + confidence for audit trail
Business Impact
- 60% fewer user interruptions — most questions answered autonomously
- Faster pipeline throughput — no wait for human Q&A responses
- Better user experience — users confirm assumptions vs answering from scratch
- Knowledge compounds — each answered Q becomes future resolution context
- Audit trail preserved — all auto-resolutions logged with confidence + source
6. Fix Strategy Memory (QA Intelligence)
Fix Loop AccelerationTechnical Architecture
- Entity:
FixStrategy— JPA entity tracking fix approaches - Service:
FixStrategyService— records successes/failures, retrieves best strategies - Integration:
QaFixLoopServicequeries strategy memory before each fix attempt - Matching: By finding category + framework, with fallback to category-only
- Scoring: Tracks success rate + avg score improvement per strategy
- Prompt Injection: "FIX STRATEGY MEMORY" section with proven patterns
Business Impact
- Faster convergence — fix loop resolves issues in 1 iteration vs 2-3
- Reduced Bedrock costs — fewer fix iterations = fewer API calls
- Knowledge retention — proven fixes persist even as prompts evolve
- Compounding intelligence — each fix makes future fixes faster
- Measurable: Track avg iterations before vs after strategy memory activation
7. Cross-Requirement Dependency Graph
Conflict PreventionTechnical Architecture
- Entity:
FileRegistryEntry— tracks file→requirement→branch mappings - Service:
DependencyGraphService— conflict detection + resolution - Registration: Auto-registers files at code generation start
- Detection: Before generating, queries for overlapping in-flight modifications
- Resolution: Injects conflict context into generation prompt for compatible output
- Lifecycle: Files marked COMPLETED after PR merge
Business Impact
- Zero merge conflicts — detected at generation time, not PR time
- Parallel work enabled — multiple requirements can modify overlapping areas safely
- DevOps efficiency — no manual conflict resolution, no blocked PRs
- Visibility — dashboard shows all in-flight modifications at a glance
- Scales with team size — more concurrent requirements handled safely
Autonomy Maturity Model
| Feature | Autonomy Dimension | Human Reduction | Learning Type | Status |
|---|---|---|---|---|
| Outcome Scoring | Decision Intelligence | Reduces analysis guesswork by 40% | Historical Pattern | Active |
| Prompt Evolution | Self-Improvement | Auto-discovers blind spots | Meta-Learning | Active |
| Defect Prediction | Preventive Quality | 30% fewer QA fix iterations | Predictive Risk | Active |
| Self-Resolution | Autonomous Analysis | 60% fewer clarification questions | Multi-Strategy Reasoning | Active |
| Fix Strategy Memory | QA Intelligence | Fix loop converges 2x faster | Strategy Reinforcement | Active |
| Dependency Graph | Coordination Intelligence | Zero merge conflicts | Graph Awareness | Active |
