Google's public Agent evaluation path: from manually reading Traces to release gates

The Google developer blog published an Agent evaluation path that moves step by step from key user journeys, simulated users, and manual Trace review to automated evaluation and merge gates. The article points out that an Agent may fall into loops, repeatedly call tools, and consume large amounts of Tokens, yet still ultimately give a fluent answer, so asserting only on the final output is not enough to prove it can go live. The article breaks the path into Day 0: manually reading Traces of 10 to 20 high-risk chains; Day 1: four layers consisting of unit tests and deterministic metrics, LLM Judge, and integration tests; and Day 2: two gates, an overall baseline and zero regression on core risk items, and gives a minimal assertion example based on pytest.