AI Reward Design: Stop Optimizing the Wrong Outcome
Read OriginalThis article explains how to design AI reward and evaluation contracts that align with true business outcomes rather than easy-to-collect metrics. Using a support workflow example, it shows how to separate mandatory permissions, verified results, escalation, and efficiency, and how to keep missing evidence visible. It covers specification gaming, the difference between training rewards and release metrics, and practical steps to define success, verification, finality, and shortcuts.
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