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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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