Equation 17 · The Hardest Unsolved Problems in AI Agent Evaluation and Reliability
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the naive estimate over n independent trials. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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where scales each failure by how costly it actually was. Two agents can share an identical of, say, ninety-five percent, while one of them fails harmlessly - an unhelpful but reversible answer - and the other fails catastrophically - an irreversible transaction, a deleted repository, a wrong medical dosage recommendation - on that same five percent. No standard agent benchmark publishes R , because assigning a defensible requires a judgment about real-world consequence that a replayable, sandboxed task suite is not built to carry, and because the tasks that would carry the highest weights are, not coincidentally, the ones too risky to include in an automated benchmark at all.
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