Evaluating an AI model and evaluating an AI agent are related—but they answer fundamentally different questions. A model benchmark tests the capability of a foundation model (how well it understands language, follows instructions, or solves problems on static tasks). An agent evaluation tests the behavior of a system operating end-to-end—planning, calling tools, handling uncertainty, and completing real workflows in a dynamic environment.
This post explains the key differences between model and agent evaluation and walks through five practical tips for evaluating AI agents as production systems. This evaluation approach focuses on trajectories, tools, and outcomes—not just model scores.
What’s the difference between evaluating an AI model and evaluating an AI agent?
While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.
AI model evaluation: The capabilities baseline
