AI Agent Evaluation Needs Shift to Behavioral Testing

As artificial agentic systems increasingly interact with dynamic environments, pursue goals, and adapt over time, they function as true behavioral systems. However, current evaluation methods predominantly focus on measuring performance outcomes, often neglecting the underlying behavioral processes that lead to those results. This paper advocates for a fundamental shift in AI evaluation. Drawing insights from the behavioral sciences, the authors argue that AI agents must be assessed in a manner similar to other behavioral systems: through meticulous observation, controlled perturbation, and careful interpretation of their actions. This approach moves beyond simply checking if an AI achieves a goal, to understanding how it achieves it and why it behaves in certain ways. The paper outlines a research agenda aimed at developing rigorous behavioral tests for AI. This includes methods for inferring decision strategies from sequences of actions, constructing environments specifically designed to highlight behavioral differences, and probing emergent dynamics within multi-agent systems. Collectively, these directions provide a roadmap for establishing a scientific discipline focused on AI behavior.