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Building agents that take real-world actions is impressive, but ensuring they continue to behave correctly as they evolve is a much harder challenge. Manual reviews of execution traces and subjective “vibe checks” quickly become a development bottleneck.

In this fourth episode of the Google ADK series, the focus moves from deployment to evaluation and quality assurance.

You’ll learn:

* What should be evaluated in an agent system? Distinguishing final-response quality from tool use, execution trajectories, state handling, and end-to-end task success.

* How Google ADK represents evaluations. Understanding eval sets, evaluation cases, invocations, evaluation configs, and the metrics that operate on them.

* How to create, run, and inspect ADK evaluations. Recording evaluation cases, configuring metrics, executing evals from the CLI or Python, and interpreting the resulting scores and reports.

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