a5c-ai - Define test cases with known-correct outputs
- Run agent against each test case
- Score: accuracy, completeness, relevance
- Compare against baseline performance
- Track performance over time
2. Skill Quality Testing
- Verify skill instructions produce expected outcomes
- Test edge cases and boundary conditions
- Measure consistency across multiple runs
- Check for harmful or incorrect outputs
- Validate against ground truth
3. Regression Suite
- Collection of previously-passing test cases
- Run after any agent/skill modification
- Flag regressions with before/after comparison
- Maintain pass rate threshold (>= 95%)
4. Process Verification
- End-to-end process execution with known inputs
- Verify each phase produces expected outputs
- Check task ordering and dependency satisfaction
- Measure total execution time
Quality Scoring
Accuracy Score (0-100)
- Correctness of output vs expected
- Partial credit for partially correct outputs
- Penalty for hallucinated or fabricated content
Completeness Score (0-100)
- Coverage of required output elements
- Missing sections flagged and scored
- Bonus for useful additional context
Consistency Score (0-100)
- Run same input 3 times
- Compare outputs for semantic similarity
- Flag inconsistencies
Composite Score
- (accuracy * 0.4 + completeness * 0.3 + consistency * 0.3)
- Threshold: 80 to pass
When to Use
- After creating new agents or skills
- After modifying existing agents or skills
- Periodic quality audits
- Before promoting skills to production
Agents Used
- Used by process-level evaluation orchestrators
- No specific agent dependency (evaluates other agents)