AI agent decision logs proposed to audit automated actions without storing full reasoning

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A guide on DEV Community proposes creating structured AI agent decision logs for auditing automated systems. These logs would document key details like the task, relevant policy, evidence, chosen action, and verified outcome when an AI makes a consequential decision. The purpose is to provide a reliable audit trail for engineers and operators without storing the model's full, private chain of thought. The recommended logs are designed to be compact and answer why an action was allowed and what changed, separate from performance traces or request logs.

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