Google Developers Blog outlines a layered eval pipeline for AI agents
Overview
Google Developers Blog says a manual "vibe check" misses agent failures such as retry loops, extra tool calls, and plausible but wrong answers.
It recommends building evals in layers: start with critical user journeys, simulated users, and a frozen golden dataset, then add unit tests, deterministic metrics, LLM-as-a-judge rubrics, and integration tests.
The post demonstrates the approach with the open-source agent-eval toolkit and a LangGraph agent example.
Written by AI from the articles below · updated Oct 15, 11:04 AM ET
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- Google Developers BlogOfficialGoogle Developers Blog outlines a repeatable eval pipeline for AI agents
AIGoogle Developers Blog says a vibe check misses agent failures such as retry loops, extra tool calls, and wrong answers that still look plausible. It recommends starting with critical user journeys, simulated users, and a frozen golden dataset, then layering unit tests, deterministic metrics, LLM-as-a-judge rubrics, and integration tests. The post uses the open-source agent-eval toolkit and a LangGraph agent example.
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