' It introduces a new 'Minimal Explanation Packet' framework to audit the long-term trajectories of autonomous agents. By shifting the focus from input features to reasoning traces and tool calls, the authors provide a more reliable way to diagnose failure modes in multi-step workflows.
This paper distinguishes between two paradigms in AI explanation: static prediction and agentic trajectories. In agentic systems, behavior emerges as a sequence of observations, reasoning steps, and actions, requiring a shift from feature-based attributions to trajectory-level explanations. We introduce the Minimal Explanation Packet (MEP) framework to provide verifiable reasoning traces for autonomous agents. This architecture separates execution from verification, allowing for deep auditing of tool calls and state updates in high-stakes environments.
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