Google shares five rules for building trustworthy AI agent evaluations

A Google Developer Relations team has published guidelines for designing reliable evaluations of AI agents, drawing from their work building Agent Skills for Google products on GitHub. The team warns that poorly designed evaluations waste token budgets and generate misleading performance signals, much like deploying an API without unit tests. They recommend understanding the constraints of your evaluation framework — such as Harbor or Inspect AI — before writing any tests, including how sandboxes, tools, and output capture work. To ensure evaluations are meaningful, the team advises writing prompts that require multi-step reasoning and reflect real-world complexity, so results genuinely reflect the agent tool's value rather than the base model's existing knowledge. The guidance is part of a broader series on scaling AI tools beyond informal 'vibe testing' toward structured, automated benchmarking pipelines.