Why Aggregate AI Benchmarks Can Hide Critical Individual Failures

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A developer building an LLM-powered customer support agent discovered that a rule designed to fix one bug introduced a new, serious flaw. The fix correctly identified policy questions, improving overall intent accuracy and answer rates, but caused the system to misclassify legitimate refund requests phrased as questions — leaving real customer claims unprocessed. Aggregate metrics showed a net improvement across four of five measured properties, masking the fact that one specific scenario was now completely broken. The author argues that per-scenario regression testing, not just averaged scores, is essential when evaluating AI model changes or code updates. A strong overall benchmark can still conceal a total capability loss for any user who happens to phrase their request politely.

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