Workshop on Agent Behavior

The dominant paradigm in AI agent research is heavily capability-centric. Agents are primarily evaluated on what they achieve, and progress is measured by rigidly closing the gap between observed and desired performance. Though this has undoubtedly driven remarkable advances, it represents an incomplete account of agency that completely sidesteps a critical question: how do agents achieve what they achieve? A system that achieves a goal through opaque, brittle, or socially harmful processes raises deep concerns that traditional test metrics simply do not capture.

We propose a workshop centered on this complementary question, which we refer to as the study of agent behavior. This encompasses the full range of both observable and latent processes governing an agent's actions, including its decision-making strategies, its interaction patterns, its internal representations, and its responses to interventions.