Position: Behavioral Systems Require Behavioral Tests
06:00 · August 20, 2026 · arXiv cs.AI RSS
Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation methods largely focus on performance outcomes, not the underlying behavioral processes that produce them. This paper argues that AI agents must be evaluated like other behavioral systems: through systematic observation, perturbation, and interpretation of their actions. We draw on lessons from the behavioral sciences to motivate this position, and propose a research agenda focused on developing rigorous behavioral tests. These include methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi-agent systems. Taken together, these directions offer a roadmap for developing a science of AI behavior.
Summary
Artificial agentic systems, such as LLM-based agents operating in tool-augmented environments like the web or operating systems, function as dynamic behavioral systems. They interact with non-stationary settings, generate sequences of actions over time, and adapt based on intermediate outcomes. Unlike static models that map inputs to outputs in a single step, these agents produce open-ended trajectories that cannot be fully predicted from their design alone.
Current evaluation practices emphasize scalar performance metrics on fixed tasks. The authors note that this focus on endpoints often conceals equifinality, where agents reach similar results through qualitatively different strategies, constraints, or internal processes. They argue that evaluation should instead draw on methods from the behavioral sciences, centering systematic observation, controlled perturbation, and interpretation of actions to reveal decision strategies, environmental influences, and potential misalignments.
The proposed research agenda includes three main directions. One involves techniques to recover underlying decision strategies from observed action sequences. Another centers on the design of controlled environments that isolate specific behavioral differences. A third examines emergent dynamics in multi-agent interactions. These approaches aim to complement existing work in interpretability and benchmarking by emphasizing the relationship between agents and their environments rather than internal representations or formal verification alone.
The position distinguishes behavioral AI systems from earlier software agents by highlighting their broader, less-specified action spaces and partially observable, stochastic settings. It calls for new theory and methods tailored to these artificial systems while acknowledging precedents in psychology, ethology, and economics.
Why it matters
The article is highly relevant for Dutch AI researchers and practitioners focused on ethical and transparent AI. By proposing behavioral tests to evaluate AI alignment, safety, and decision-making processes, it provides a crucial methodological framework that supports compliance with EU regulations like the AI Act and advances responsible AI deployment.
