What is an Agent Evaluation Framework?
The components of a system for evaluating AI agents — datasets, graders, metrics, and the harness that ties them together.
An agent evaluation framework is the structured system a team uses to measure whether an AI agent does its job: a dataset of real cases, graders that score each run (rule-based checks, LLM-as-a-judge, and human review), metrics that roll those scores up, and a harness that runs the whole thing on every change and on a sample of live traffic. It turns 'is the agent good?' from a one-off judgement into a repeatable, versioned measurement.
What are the components of an agent evaluation framework?
Four parts, working together. A dataset of real cases — the inputs the agent actually sees, including the sessions that went wrong, with an expected outcome or reference for each. Graders that score each run: exact-match and rule-based checks for the deterministic parts, an LLM-as-a-judge for subjective qualities like correctness and groundedness, and human review to calibrate the judges. Metrics that aggregate the scores into trackable numbers — pass rate, quality score, regression rate, hallucination rate, cost and latency. And a harness that runs the agent over the dataset, applies the graders, and reports the metrics, both offline on every change and online against live traffic.
The framework is the thing that makes all four repeatable and versioned, so quality is a trend you can plot rather than an argument you have in chat.
How is it different from a model evaluation framework?
A model evaluation framework scores a model in isolation, usually at development time, against a fixed benchmark. An agent evaluation framework scores the whole agent — the model plus its tools, retrieval, memory and multi-step plan — against real production behaviour. The agent can call the wrong tool, lose context across steps, or take an unauthorised action, none of which a model benchmark measures. So the framework has to capture full sessions, not single completions, and run continuously, not just before launch.
What metrics belong in an agent evaluation framework?
Score the properties that decide whether the agent is trustworthy: task completion (did it actually resolve the request end to end), groundedness (were its claims supported by the retrieved context), tool-use correctness (right tool, valid arguments), policy adherence (did it stay in scope), and operational quality (cost, latency, tokens per session). Track each per agent and per version so a change's effect is visible. A single accuracy number hides where an agent is failing; a handful of targeted metrics shows you what to fix.
Should you build or buy an agent evaluation framework?
Start smaller than you think. Twenty real cases, scored the same way every time, is already a framework — no platform required. Open-source libraries (DeepEval, Ragas, OpenAI Evals) give you graders and a test harness as code for the offline side. You assemble the production side — sampling live traffic, dashboards, alerting, per-agent quality trends — yourself, or use a platform built for it. The right choice depends on your gap: if you lack offline testing, adopt a framework this week; if you lack production quality visibility, that is what a dedicated agent platform is for.
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