AI Agent Knowledge Library

Evaluation

Methods for evaluating AI agents, including task success, benchmarks, regression testing, LLM-as-judge approaches, and quality measurement.

3 Published resources

Explore: Security & Safety Reliability Cost & Performance Observability Deployment

Editor’s pick

Start with this guide

Side-by-side comparison of LLM evaluation from input to model output and agent evaluation from goal through model, tool, observation, state, action, and outcome.

Comparison Intermediate

LLM Evaluation vs Agent Evaluation

LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.

Knowledge library

Latest resources

3 published articles

Agent evaluation pipeline from tasks and agent runs through traces and outcomes, evaluators, scores, failure analysis, and improvement.

Tutorial Intermediate

How to Evaluate an AI Agent

A practical workflow for defining agent success, building evaluation datasets, capturing traces, scoring behavior, analyzing failures, and preventing regressions.

AI agent evaluation stack showing goal, outcome, trajectory, tool calls, constraints, and reliability, cost, and latency.

Explainer Beginner

AI Agent Evaluation Explained

Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.