Interpreting Agent Behavior (IAB): Human-Centered Interpretation for Understanding Agents, Humans, and Interaction

Commercial autonomous agents such as Claude and Codex now run for hours or even days to complete tasks, and along the way they show complex behavior: they plan, reason, use tools, recover from errors, coordinate with subagents, and communicate with users. We use the word behavior, as in the study of human behavior, for the full range of what an agent does during runtime. This behavior spans three levels: what agents do and how they do it (decomposing tasks, deciding under uncertainty), what people do in response (instructing, verifying, stepping in), and how the two work together through instructions and corrections. All three levels are generating vast behavioral data such as execution logs and interaction traces. Yet existing approaches read this data largely for outcomes, not through a behavior lens: benchmarks tell us whether an agent succeeds or fails, but not what it did or how it did it. Understanding what and how is what people actually need. It lets agent developers and model trainers debug failures, compare architectures, and filter training data. It lets agent users and deployment engineers watch production agents to understand safety, cost, and reliability risks. Our purpose with IAB is to identify the emerging problem spaces and challenges, pushing the community towards building this missing layer between raw behavioral data and meaningful human oversight: debugging, governance, and calibrated trust all depend on first understanding what an agent did and how.