As part of my work at Google, we are publishing a suite of Agent Skills for Google products and technologies on GitHub：https://github.com/google/skills. These agent skills：https://agentskills.io/home 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 ：https://dev.to/googleai/designing-ai-evals-clarity-now-and-visualization-next-4eii 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.
Here are five rules we learned to design better evaluations you can trust. Follow them to ensure that every token you spend produces a useful metric.
Before writing evaluations, you need to understand the setup and limitations of your chosen framework. This includes systems like Harbor, Inspect AI, or integrations in development tools like in the Agent Development Kit：https://docs.cloud.google.com/gemini-enterprise-agent-platform/optimize/evaluation/agent-evaluation. Does it use an ephemeral sandbox? What tools are available? How is the output captured
If your evaluations show a high baseline accuracy (i.e., without your agent tool), it might not prove its value, or the evaluation prompts are too easy.
You cannot grade an agent on something you did not explicitly ask it to do. Your evaluation prompts and graders should be complementary. This means that they should only test for things included in the prompt.
Agents possess inherent model knowledge and might skip your custom tools entirely to arrive at the correct answer. (That on its own is some useful feedback!)
A strong evaluation suite tests real, diverse use cases. But testing the same capability repeatedly causes overfitting and creates noisy metrics.
You cannot improve AI tools if you can't measure them accurately. If you treat AI evaluations with the same focus as traditional unit tests, you improve the quality of your metrics and get more robust signals.
By applying these five rules, you eliminate false signals that waste your token budget. Instead of generating noise, your test suite gives you actionable feedback you can use to guide your engineering decisions and improve your tools.
Figuring out what to test is only the first step. A well-designed evaluation is only useful if the scorer grading answers is reliable and returns meaningful results. In my next post, we will look at how to test. You will learn how to write lean, atomic rubrics that minimize ambiguity for an LLM grader and make every token count.
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