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Jason Woltje 9f3c76d43b test(#146 ): Validate assignment cost optimization

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>

2026-02-01 18:13:53 -06:00

__init__.py

feat(#157 ): Set up webhook receiver endpoint

2026-02-01 17:41:46 -06:00

conftest.py

feat(#160 ): Implement basic orchestration loop

2026-02-01 18:03:12 -06:00

test_agent_assignment.py

test(#146 ): Validate assignment cost optimization

2026-02-01 18:13:53 -06:00

test_agent_profiles.py

feat(#144 ): Implement agent profiles