Behavioral evaluations provide a framework for testing and validating the decision-making processes of AI agents. Unlike standard functionality tests, these evaluations focus on how agents choose tools and make decisions based on prompts. This is crucial for ensuring that modifications to prompts or tools do not inadvertently degrade performance or lead to unexpected behaviors. The skill offers a structured approach to creating, running, fixing, and promoting these evaluations, making it an essential tool for developers working with AI agents.

The workflow begins with determining whether a prompt or tool change requires validation. If so, the next steps involve selecting the appropriate evaluation method based on the nature of the interaction—whether it is UI-heavy or not. The skill provides clear guidance on setting up tests, including how to seed workspaces with realistic scenarios and write assertions that effectively audit agent decisions. This ensures that the evaluations are thorough and accurately reflect the agent's capabilities.

In addition to creating new tests, the skill also addresses how to fix failures and promote tests that have been validated. This is particularly useful for maintaining the integrity of the agent's decision-making logic over time. With bundled resources like procedural guides for creating, fixing, and promoting evaluations, users have access to comprehensive documentation that supports them at every stage of the evaluation process.

Overall, Behavioral Evaluations is designed for developers and teams who need to ensure the reliability and correctness of AI agent behaviors. By implementing these structured tests, they can confidently deploy updates and modifications while minimizing the risk of regressions or failures in decision-making.