An article argues that agent evaluation systems should provide more than just a pass/fail grade. It suggests that evaluations should include detailed evidence, such as the model used, prompt version, tool surface, fixture state, expected and actual behavior, cost, latency, and the evaluator's decision with a reason code. This detailed record, referred to as a "decision receipt," is crucial for understanding why an agent passed or failed, moving beyond a simple label to a diagnostic tool. The author highlights Armorer Guard and Armorer as projects aiming to implement these more transparent and inspectable evaluation processes. AI
IMPACT Enhances transparency and debuggability in AI agent development by advocating for detailed evaluation records.
RANK_REASON The item is an opinion piece discussing best practices for AI agent evaluation systems.
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