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Agent Evaluation

A testing framework that scores a multi-turn AI agent behavior (not just final output) across goal completion, trajectory efficiency, tool-call correctness, and safety.

What Is Agent Evaluation?

Single-call LLM evals do not catch agent failure modes (looping, wrong tool, off-policy steps). Agent evals replay or simulate full conversations and score the trajectory: did the agent reach the goal, in how many steps, with the right tools, without violating constraints. Frameworks include AgentBench, LangSmith trajectory evals, and DeepEval agent metrics.

How Groovy Web Uses This

We build agent-eval suites alongside the agent itself. Every prompt or tool change runs against the eval set before merge.

Related Terms

AI Agent LLM Evals LLM Observability Multi-Agent System

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