Add comprehensive cost optimization test scenarios and validation report.
Test Scenarios Added (10 new tests):
- Low difficulty assigns to MiniMax/GLM (free agents)
- Medium difficulty assigns to GLM when within capacity
- High difficulty assigns to Opus (only capable agent)
- Oversized issues rejected with actionable error
- Boundary conditions at capacity limits
- Aggregate cost optimization across all scenarios
Results:
- All 33 tests passing (23 existing + 10 new)
- 100% coverage of agent_assignment.py (36/36 statements)
- Cost savings validation: 50%+ in aggregate scenarios
- Real-world projection: 70%+ savings with typical workload
Documentation:
- Created cost-optimization-validation.md with detailed analysis
- Documents cost savings for each scenario
- Validates all acceptance criteria from COORD-006
Completes Phase 2 (M4.1-Coordinator) testing requirements.
Fixes#146
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements the Coordinator class with main orchestration loop:
- Async loop architecture with configurable poll interval
- process_queue() method gets next ready issue and spawns agent (stub)
- Graceful shutdown handling with stop() method
- Error handling that allows loop to continue after failures
- Logging for all actions (start, stop, processing, errors)
- Integration with QueueManager from #159
- Active agent tracking for future agent management
Configuration settings added:
- COORDINATOR_POLL_INTERVAL (default: 5.0s)
- COORDINATOR_MAX_CONCURRENT_AGENTS (default: 10)
- COORDINATOR_ENABLED (default: true)
Tests: 27 new tests covering all acceptance criteria
Coverage: 92% overall (100% for coordinator.py)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add Capability enum (HIGH, MEDIUM, LOW) for agent difficulty levels
- Add AgentName enum for all 5 agents (opus, sonnet, haiku, glm, minimax)
- Implement AgentProfile data structure with validation
- context_limit: max tokens for context window
- cost_per_mtok: cost per million tokens (0 for self-hosted)
- capabilities: list of difficulty levels the agent handles
- best_for: description of optimal use cases
- Define profiles for all 5 agents with specifications:
- Anthropic models (opus, sonnet, haiku): 200K context, various costs
- Self-hosted models (glm, minimax): 128K context, free
- Implement get_agent_profile() function for profile lookup
- Add comprehensive test suite (37 tests, 100% coverage)
- Profile data structure validation
- All 5 predefined profiles exist and are correct
- Capability enum and AgentName enum tests
- Best_for validation and capability matching
- Consistency checks across profiles
Fixes#144
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements ContextMonitor class with real-time token usage tracking:
- COMPACT_THRESHOLD at 0.80 (80% triggers compaction)
- ROTATE_THRESHOLD at 0.95 (95% triggers rotation)
- Poll Claude API for context usage
- Return appropriate ContextAction based on thresholds
- Background monitoring loop (10-second polling)
- Log usage over time
- Error handling and recovery
Added ContextUsage model for tracking agent token consumption.
Tests:
- 25 test cases covering all functionality
- 100% coverage for context_monitor.py and models.py
- Mocked API responses for different usage levels
- Background monitoring and threshold detection
- Error handling verification
Quality gates:
- Type checking: PASS (mypy)
- Linting: PASS (ruff)
- Tests: PASS (25/25)
- Coverage: 100% for new files, 95.43% overall
Fixes#155
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
