Extend the existing Apprentice system to support multi-step story learning while maintaining backward compatibility with atomic task routing.
- Routes requests between frontier API and local model
- Collects training data as request/response pairs
- Handles atomic exchanges effectively
- Has 2628 existing tests that must continue passing
Add journey-level optimization capabilities including:
- Multi-turn conversation flow handling
- Per-journey-type phase transition tracking
- Goal completion detection and measurement
- Step efficiency analysis
- Backtracking pattern recognition
- Multi-step consistency scoring
- Backward Compatibility: All existing atomic task routing must remain unaffected
- Opt-in Configuration: Story learning enabled via
story_learning_enabled: true - No New Dependencies: Work within existing Python 3.12+ and Pydantic v2 constraints
- Test Preservation: All 2628 existing tests must pass
- Frozen Models: Maintain existing model constraints
- Story Model: Represents multi-step narratives with metadata
- StoryStep Model: Individual steps within a story journey
- StoryCollector: Aggregates and processes story data from Chronicler
- JourneyEvaluator: Analyzes journey patterns and efficiency metrics
- Enhanced Phase Manager: Extended for per-journey-type tracking
- Chronicler will emit multi-step event narratives
- StoryCollector processes these narratives into training data
- JourneyEvaluator provides optimization insights
- Phase manager tracks journey-specific transitions
- Journey completion rates by type
- Multi-turn consistency scores
- Step efficiency measurements
- Backtracking frequency analysis
- Goal achievement tracking