AI Agent Evaluation

AI agent evaluation studies how well systems use tools and interact with changing environments to complete tasks, from coding and web navigation to scientific and professional workflows. Researchers build interactive benchmarks and assess outcomes alongside execution traces, using deterministic state checks, expert rubrics, and language-model judges. Repeated trials and process-level measures help reveal failures in tool use, safety, consistency, and recovery that a final pass rate can hide. A central challenge is ensuring that benchmark scores reflect real capability rather than leakage, reward hacking, evaluator bias, or quirks of a particular model harness.