As part of my work at Google, we are publishing a suite of Agent Skills for Google products and technologies on GitHub. These agent skills are designed to help AI agents interact with our technologies. But how do you test that these skills are useful and work as expected? My team in Developer Relations has been focused on this question, because having reliable signals on their performance is critical to help us improve them over time.

Just as you wouldn't deploy a production API without writing unit tests, you should apply the same standard to your AI agents. As Joe Spiro showed in the Designing AI Evals post series, scaling AI tools means moving beyond "vibe testing" in a terminal. Instead, you should set up a structured, automated evaluation pipeline to benchmark your integration. The evaluations (evals) are the actions you asked the agent to perform, which are graded using scorers (for example rubrics) that assert whether the agent succeeded. We'll focus on evaluations in this post and tackle tips for scoring rubrics in the next post.

However, AI evaluations cost real tokens. You need to make sure that you use these tokens as efficiently as possible. They need to provide real value that helps you build better tools. Writing good evaluations is critical. Poor evaluations provide false signals, waste your token budget, and create noise in your metrics.