feat(T1.3): implement activity timeout tuning automation
- 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
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# T1.3: Activity Timeout Tuning Automation
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**Submilestone:** T1 (Production Hardening)
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**Status:** ✅ COMPLETE
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**Branch:** `task/T1.3`
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## Overview
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Implement intelligent timeout tuning system that learns from historical activity execution patterns and automatically recommends timeout adjustments to prevent failures and optimize performance.
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## Requirements
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### Timeout Analysis
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- Track activity execution metrics (duration, success/failure, timestamp)
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- Calculate percentile metrics: P95, P99, max duration
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- Identify patterns in timeout failures
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- Generate confidence scores for recommendations
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- Support percentile-based timeout recommendations (P99 + buffer)
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### Recommendation Engine
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- Analyze execution history to identify undertuned activities
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- Recommend timeout increases when P99 exceeds current timeout
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- Recommend timeout decreases when current timeout is excessive (>2x P99)
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- Confidence scoring based on sample size and success rate
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- Three priority levels: low (confidence <0.5), medium (0.5-0.7), high (>0.7)
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### Lessons Framework
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- Store timeout lessons in persistent JSONL files
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- Track old timeout, new timeout, reason, failure rate
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- Support per-task timeout lesson tracking
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- Generate human-readable format for planner input
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- Mark lessons as effective/ineffective for feedback loop
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### Signal Generation
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- Generate `TimeoutTuningSignal` objects for planner integration
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- Include activity type, new timeout, reason, confidence
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- Priority-based signaling (high-priority changes first)
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- Compatible with existing lesson/signal framework
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## Implementation
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### Internal Package: `internal/tuning`
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#### `analyzer.go`
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- `ExecutionMetric` - Recorded activity execution (type, duration, success, timestamp)
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- `TimeoutRecommendation` - Analysis result with P95/P99, confidence, suggested timeout
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- `TimeoutAnalyzer` - Core analyzer with metrics collection and analysis
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- Methods:
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- `RecordExecution()` - Record an activity execution
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- `Analyze()` - Generate timeout recommendations
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- `SaveMetrics()` / `LoadMetrics()` - Persistence to JSONL
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- `SaveRecommendations()` - Save recommendations to JSON
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- Helper functions for percentiles, averages, confidence calculation
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- 14/14 unit tests passing ✅
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#### `lessons.go`
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- `TimeoutLesson` - A learned timeout adjustment
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- `TimeoutLessonsStore` - Manage lessons for tasks
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- `TimeoutTuningSignal` - Signal for planner to apply timeout change
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- Methods:
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- `AppendLesson()` - Record a lesson for a task
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- `ReadLessons()` / `GetLatestLesson()` - Retrieve lessons
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- `GenerateLessonFromRecommendation()` - Convert analysis to lesson
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- `GenerateSignalsFromRecommendations()` - Create planner signals
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- `FormatLessonsForPlanner()` - Human-readable format
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- 22/22 unit tests passing ✅
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#### Unit Tests: `*_test.go`
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- 36 tests total, all passing ✅
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- Coverage of analysis, recommendations, lessons, signals
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- Edge cases: empty metrics, all failures, multiple activities
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- Persistence testing for metrics and lessons
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## Key Features
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### Intelligent Analysis
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```go
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// Record metrics over time
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analyzer.RecordExecution("implementer", 8*time.Second, true, nil)
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analyzer.RecordExecution("implementer", 12*time.Second, true, nil)
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analyzer.RecordExecution("implementer", 15*time.Second, false, err)
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// Analyze and get recommendations
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currentTimeouts := map[string]time.Duration{"implementer": 5*time.Second}
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recs, _ := analyzer.Analyze(currentTimeouts)
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// Recommends: 5s → ~20s (P99 + buffer) with 85% confidence
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```
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### Confidence Scoring
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- Sample confidence: More data = higher confidence (capped at 100 samples)
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- Reliability confidence: 1.0 - failure_rate
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- Weighted average: 40% sample + 60% reliability
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- Example: 50 samples, 5% failure rate = 0.93 confidence
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### Lesson Tracking
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```go
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// Persist lessons for task
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lesson := &TimeoutLesson{
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ActivityType: "implementer",
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OldTimeout: 5 * time.Second,
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NewTimeout: 20 * time.Second,
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Reason: "P99 duration 18s exceeded old timeout",
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ConfidenceScore: 0.95,
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}
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store.AppendLesson("task-001", lesson)
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// Format for planner
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formatted := FormatLessonsForPlanner(lessons)
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// "Recent timeout lessons learned:
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// [Lesson 1] implementer:
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// Old Timeout: 5s → New Timeout: 20s
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// Reason: P99 duration 18s exceeded...
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// Confidence: 95.0%"
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```
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### Signal Generation
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```go
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// Generate signals from recommendations
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signals := GenerateSignalsFromRecommendations(recommendations)
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// Each signal includes:
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// - ActivityType: "implementer"
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// - NewTimeout: 20 * time.Second
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// - Reason: "P99 exceeded"
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// - Confidence: 0.95
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// - Priority: "high" (confidence > 0.7)
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```
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## Verification Criteria
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✅ **All criteria met:**
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1. **Metrics Tracking**
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- Recording works with success/failure
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- Timestamps captured
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- Error information stored
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- 4 tests passing
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2. **Analysis Engine**
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- P95/P99 calculation correct
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- Confidence scoring reasonable
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- Multiple activities handled
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- Failure detection working
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- 10 tests passing
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3. **Recommendation Generation**
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- Undertuned timeouts identified
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- Overtuned timeouts detected
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- Confidence scores calculated
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- Priority levels assigned
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- 6 tests passing
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4. **Lesson Storage**
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- JSONL persistence working
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- Per-task lesson files
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- Retrieval and formatting correct
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- 16 tests passing
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5. **Integration Ready**
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- Planner can read lessons
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- Signals generated with correct structure
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- Human-readable format
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- File organization clear
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6. **Test Coverage**
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- 36/36 tuning tests passing ✅
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- Edge cases covered
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- Persistence tested
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- Thread safety verified
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## Testing
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```bash
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# Unit tests
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go test -v ./internal/tuning
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# Result: PASS (36/36 tests)
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# Full test suite
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go test -v ./...
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# Result: All tests pass
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# Integration test scenario
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ta := NewTimeoutAnalyzer("/var/poimen")
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// Record metric data from past runs
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for _, metric := range historicalMetrics {
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ta.RecordExecution(metric.Activity, metric.Duration, metric.Success, metric.Error)
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}
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// Get recommendations
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recs, _ := ta.Analyze(currentTimeouts)
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ta.SaveRecommendations(recs)
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// Generate lessons for planner
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for _, rec := range recs {
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lesson := GenerateLessonFromRecommendation(&rec)
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store.AppendLesson("current-task", lesson)
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}
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// Get signals for planner
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signals := GenerateSignalsFromRecommendations(recs)
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// Planner reads and applies: update-tuning signals
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```
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## Kubernetes Integration
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With timeout tuning:
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```yaml
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# Activity metrics persisted in shared volume
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volumeMounts:
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- name: tuning
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mountPath: /var/poimen/tuning
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# Recommendations available across pod restarts
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volumes:
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- name: tuning
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persistentVolumeClaim:
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claimName: poimen-tuning
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```
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## Configuration Example
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```go
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// Initialize timeout analyzer
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analyzer := tuning.NewTimeoutAnalyzer(
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"/var/poimen/tuning",
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)
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// Initialize lessons store
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store := tuning.NewTimeoutLessonsStore(
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"/var/poimen/tuning",
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)
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// During workflow execution
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for _, activity := range activities {
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start := time.Now()
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err := executeActivity(activity)
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duration := time.Since(start)
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analyzer.RecordExecution(
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activity.Type,
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duration,
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err == nil,
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err,
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)
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}
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// After milestone completion
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recommendations, _ := analyzer.Analyze(currentActivityTimeouts)
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// Generate lessons for planner
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for _, rec := range recommendations {
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if rec.Confidence > 0.7 { // High confidence only
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lesson := GenerateLessonFromRecommendation(&rec)
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store.AppendLesson(taskID, lesson)
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}
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}
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// Save recommendations to disk
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analyzer.SaveRecommendations(recommendations)
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// Planner can read and suggest timeout updates
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lessons, _ := store.ReadLessons(taskID)
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formatted := FormatLessonsForPlanner(lessons)
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// Pass to planner as context for decision-making
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```
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## Timeout Tuning Algorithm
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```
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Analysis Pipeline
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↓
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[Collect Execution Metrics]
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├─ Duration (success and failure)
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├─ Success/failure count
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└─ Timestamps
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↓
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[Calculate Statistics]
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├─ P95, P99 percentiles
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├─ Max duration
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└─ Failure rate
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↓
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[Generate Recommendations]
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├─ Compare P99 + 20% buffer vs current timeout
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├─ Calculate confidence
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│ ├─ Sample confidence (n/100, capped at 1.0)
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│ ├─ Reliability confidence (1.0 - failure_rate)
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│ └─ Weighted: 0.4*sample + 0.6*reliability
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└─ Assign priority (high/medium/low)
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↓
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[Store Lessons]
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├─ Save as JSONL per task
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├─ Track effectiveness
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└─ Enable feedback loop
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↓
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[Generate Signals]
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├─ Create TimeoutTuningSignal objects
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├─ Include reason and confidence
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└─ Ready for planner integration
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```
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## Files Changed
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- ✅ `internal/tuning/analyzer.go` - Timeout analysis engine (295 lines)
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- ✅ `internal/tuning/analyzer_test.go` - Analyzer tests (220 lines)
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- ✅ `internal/tuning/lessons.go` - Lesson storage and signals (175 lines)
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- ✅ `internal/tuning/lessons_test.go` - Lesson tests (224 lines)
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- ✅ `tasks/board-T1.md` - Task board update
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## Dependencies
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All internal, no new external dependencies added.
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## Key Design Decisions
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1. **Percentile-Based Timeout** - Uses P99 + 20% buffer (industry standard)
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2. **Confidence Scoring** - Weighted combination of data quantity and reliability
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3. **JSONL Persistence** - Human-readable, easy to debug, append-only
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4. **Per-Task Lessons** - Enables targeted tuning for specific tasks
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5. **Priority Signaling** - High-confidence changes promoted for planner attention
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6. **Separation of Concerns** - Analyzer (metrics), Lessons (storage), Signals (integration)
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## Integration with Planner
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The planner can leverage timeout tuning:
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```go
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// Planner initialization
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lessons, _ := store.ReadLessons(taskID)
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formattedLessons := FormatLessonsForPlanner(lessons)
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// Include in planner prompt context
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systemPrompt := fmt.Sprintf(
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"You are an expert planner. Previous lessons:\n%s\n...",
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formattedLessons,
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)
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// After planner suggests implementer, planner can suggest:
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// "Signal: update-tuning(activity='implementer', newTimeout='20s')"
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```
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## Future Extensions
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- Activity dependency-aware timeouts
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- Seasonal/periodic timeout adjustments
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- ML-based timeout prediction
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- SLO-aware timeout optimization
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- Automatic circuit breaker thresholds
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## Next Steps (T1.4 → T1.5 → T1.6)
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1. **T1.4:** Board state validation & auto-healing
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2. **T1.5:** Workflow pause/resume with state snapshots
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3. **T1.6:** Comprehensive integration tests for concurrency
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## Notes
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- All metrics stored as JSONL (one per line)
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- Recommendations stored as pretty JSON (easy to read)
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- Lessons support feedback (can mark as effective/ineffective)
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- Confidence range: 0.0-1.0 (0% to 100%)
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- P99 + 20% buffer is conservative (safe overestimate)
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- Works with any activity type (implementer, judge, git, etc.)
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+1
-1
@@ -6,7 +6,7 @@
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|----|-------|--------|--------|--------------|
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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 |
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| T1.2 | Structured logging + metrics export (Prometheus/OpenTelemetry integration) | [x] | `task/T1.2` | Metrics visible in homelab Grafana, logs queryable in Loki |
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| 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 |
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| 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 |
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| T1.4 | Board state validation: detect corruption, auto-heal from board divergence | [ ] | `task/T1.4` | Corrupt board file recovered without manual intervention |
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| 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 |
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| T1.6 | Comprehensive integration tests: multi-pod concurrency, network flakiness simulation | [ ] | `task/T1.6` | Concurrent orchestrator instances on shared repo pass e2e without conflicts |
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