Agent Evaluation

The process of measuring agent performance — accuracy, reliability, cost, latency — against defined benchmarks or production data.

Agent evaluation answers “is this agent good enough to ship?” and “is the new version better than the old?” — neither of which is obvious from a few demos. Public benchmarks (GAIA, SWE-Bench Verified, OSWorld, Tau-Bench) test specific capabilities; internal evals test what actually matters for the application.

Stanford’s AI Index 2026 reports a 37% average gap between lab benchmark scores and real-world deployment performance. This is the single most important insight in agent evaluation: passing public benchmarks is necessary but not sufficient. Production teams must build their own evals on their own data, with their own success criteria.