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feat(T1.3): implement activity timeout tuning automation

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- Add internal/tuning package with intelligent timeout analysis
- Implement TimeoutAnalyzer for tracking activity execution metrics
- Calculate percentile-based timeout recommendations (P95, P99)
- Generate confidence scores based on sample size and failure rate
- Implement TimeoutLessonsStore for persistent lesson tracking
- Store lessons in per-task JSONL files with effectiveness tracking
- Generate TimeoutTuningSignal objects for planner integration
- Generate human-readable lesson format for planner context
- Support three-tier priority signaling (high/medium/low)
- Analyze multiple activities concurrently

Analysis Features:
- Track duration, success/failure, timestamps for each execution
- Identify undertuned activities (P99 exceeds timeout)
- Detect overtuned activities (timeout > 2x P99)
- Calculate confidence scores (40% sample data + 60% reliability)
- Generate recommendations with reasoning

Lesson Management:
- Persist lessons per task in JSONL format
- Support lesson effectiveness tracking
- Format lessons for planner input
- Enable feedback loop for timeout optimization

Test Coverage:
- 14 analyzer tests (metrics, analysis, persistence)
- 22 lessons tests (storage, signals, formatting)
- 36 total tuning tests, all passing
- Edge cases: empty metrics, all failures, multiple activities

Key Design:
- P99 + 20% buffer for safe timeout values
- Weighted confidence scoring for reliable recommendations
- Separation: Analyzer (metrics), Lessons (storage), Signals (integration)
- Thread-safe analyzer with RWMutex
- No external dependencies added

Closes T1.3
This commit is contained in:
Test
2026-08-23 16:47:31 -07:00
parent 37ef33084e
commit 4425a29d0f
6 changed files with 1493 additions and 1 deletions
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tasks/board-T1.md
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|----|-------|--------|--------|--------------|
| T1.1 | Workflow error recovery: retry policies, deadletter handling, graceful shutdown | [x] | `task/T1.1` | Simulate orchestrator crash mid-cycle, resume without data loss |
| T1.2 | Structured logging + metrics export (Prometheus/OpenTelemetry integration) | [x] | `task/T1.2` | Metrics visible in homelab Grafana, logs queryable in Loki |
| T1.3 | Activity timeout tuning automation: learn from historical failures, recommend overrides | [ ] | `task/T1.3` | Planner reads lessons file, suggests `update-tuning` signal based on patterns |
| T1.3 | Activity timeout tuning automation: learn from historical failures, recommend overrides | [x] | `task/T1.3` | Planner reads lessons file, suggests `update-tuning` signal based on patterns |
| T1.4 | Board state validation: detect corruption, auto-heal from board divergence | [ ] | `task/T1.4` | Corrupt board file recovered without manual intervention |
| T1.5 | Workflow pause/resume with state snapshot: serialize mid-cycle state to persistent store | [ ] | `task/T1.5` | Pause signal, restart pod, resume signal → workflow continues from exact point |
| T1.6 | Comprehensive integration tests: multi-pod concurrency, network flakiness simulation | [ ] | `task/T1.6` | Concurrent orchestrator instances on shared repo pass e2e without conflicts |