testing-skills-with-subagents

Testing AI agent skills involves applying a Red-Green-Refactor methodology to skill documentation. This process simulates real-world pressures like time and sunk costs to identify rationalizations and ensure strict rule compliance.

The Red-Green-Refactor cycle for skill validation involves running baseline scenarios to observe failures, writing skill documentation to address them, and refactoring to close loopholes. This iterative process bulletproofs agent behavior against common failure modes.

Validate agent behavior by simulating scenarios where cutting corners is tempting, such as urgent fixes or tight deadlines. This pressure testing ensures your skill explicitly blocks common excuses and prevents rule violations before deployment.

Yes, you can apply TDD to AI agent documentation by iteratively refining skill logic. By testing baseline scenarios, writing rules to address observed failures, and refactoring, you build explicit counter-measures against common failure modes.

AI agents bypass skill rules under pressure due to common rationalizations like sunk cost or authority bias. Without explicit counter-measures and pressure testing during development, agents will find loopholes to cut corners in urgent scenarios.

The limitations include the requirement for iterative refinement and the challenge of anticipating every real-world pressure. You must continuously refactor skill logic to close loopholes, as static documentation cannot anticipate all rationalization paths.