feat: Implement Phase 2G.4 — Learning system integration & per-agent metrics
Per-agent request logging, feedback processing, and confidence scoring.
- Per-agent metric collection: request_id, model, latency_ms, tokens_in/out, confidence, fallback_used, success
- Agent feedback loop: outcome tracking (success/fallback/timeout/error/user_rejected)
- Confidence scoring: 50% success + 25% quality + 25% satisfaction (per-agent independent of global)
- Cost attribution: Monthly cost report per agent (tokens × model rate)
- SLO monitoring: p50/p95/p99 latencies vs per-agent targets
- Anomaly detection: σ-based latency spikes, success rate drops, confidence degradation
- Full TypeScript types, database schema initialization, comprehensive documentation
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