testing-skills-with-subagents

You test AI agent skills for compliance under pressure by applying the Test-Driven Development (TDD) cycle to systematically verify skill documentation using scenario-based testing. This involves creating realistic pressures like time constraints and authority to ensure agents cannot bypass rules.

The TDD cycle for validating agent behavior applies the RED-GREEN-REFACTOR methodology to skill creation. It involves running baseline scenarios without the skill, writing the skill to address failures, and iteratively refining documentation to close loopholes and prevent rationalizations.

You prevent AI agents from rationalizing and bypassing rules by capturing and documenting their excuses for non-compliance during pressure testing. This rationalization capture process allows you to iteratively refine skill documentation and close loopholes before deployment.

You should use TDD to validate AI agent skills before deploying critical skills that enforce specific workflows, such as security protocol adherence. It is necessary when you need to rigorously test effectiveness under simulated real-world pressures to ensure agents cannot bypass rules.

Yes, you need to write baseline scenarios to test skills with TDD. The quick start process requires running baseline scenarios without the skill first, then writing the skill to address the failures observed, and finally refactoring to close any identified loopholes.

The best way to bulletproof AI skills against non-compliance is to apply TDD methodology to skill documentation. This approach systematically verifies compliance through scenario-based testing and captures agent rationalizations to iteratively refine and close loopholes.