Google Cloud shares four lessons on evaluating its Developer plugin's agent
Overview
Google Cloud's Developer plugin team says LLM judges that grade an agent's final text miss what the agent actually did, so the team scores raw tool-call trajectories instead.
The team also says it runs live and plan-only evaluations side by side, because live runs can be blocked by permission errors before the behavior under test appears.
The team recommends golden sets curated by human experts as durable baselines. The lessons appear in a Google Cloud post on evaluation engineering for agent plugins, where the team argues that measuring an agentic system is harder than building it.
Written by AI from the articles below · updated Oct 16, 12:28 PM ET
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- Google Cloud · AI & Machine LearningOfficialGoogle Cloud shares four evaluation lessons from building its Developer plugin
AIGoogle Cloud's Developer plugin team says LLM judges that grade an agent's final text miss what it actually did, so they score raw tool-call trajectories instead. The team also says it runs live and plan-only evaluations side by side, because live runs can be blocked by permission errors before the behavior under test appears, and it recommends golden sets curated by human experts as durable baselines.
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