testing-skills-with-subagents

To test AI agent behavior under pressure, apply the RED-GREEN-REFACTOR cycle to skill development. This involves creating pressure scenarios to observe baseline failures, writing minimal skills to address them, and iteratively refining the skill to plug rationalization loopholes.

The RED-GREEN-REFACTOR cycle for skill development is a testing method where you run scenarios without a skill to observe baseline failures, write the minimal skill to fix them, and iteratively refine the skill to close loopholes identified during pressure testing.

You validate that AI skills are bulletproof against bypassing by continuously testing the skill against new rationalizations and pressures. This iterative refinement process ensures agents adhere to documented processes even when tempted to cut corners.

Yes, you can use TDD to ensure AI agents adhere to coding standards under urgent deadlines. By simulating high-pressure scenarios like manager overrides, you can verify that the agent strictly follows the documented processes instead of cutting corners.

The best way to close loopholes in AI process documentation is through iterative pressure testing. By observing agent failures and their exact rationalizations, you can continuously refine the skill until it successfully prevents all bypassing attempts.