Equation 11 · Measuring AI Agent Reliability: What the Evidence Actually Supports
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the number of samples. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Pass@k answers a specific question: given a budget of k independent attempts, what is the chance that at least one succeeds? That is the right question for a search-and-verify workflow, where a cheap checker can identify the one attempt that worked among several candidates. It is the wrong question for almost everything else an agent does, because most agentic tasks do not offer a free, cheap oracle that can pick the winning attempt out of a pile of candidates after the fact — the “attempt” is the deployment. For that setting, Yao and colleagues, building the tau-bench benchmark for tool-using agents interacting with simulated customers under domain policies, proposed the complementary…
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Pass@k answers a specific question: given a budget of k independent attempts, what is the chance that at least one succeeds? That is the right question for a search-and-verify workflow, where a cheap checker can identify the one attempt that worked among several candidates. It is the wrong question for almost everything else an agent does, because most agentic tasks do not offer a free, cheap oracle that can pick the winning attempt out of a pile of candidates after the fact — the “attempt” is the deployment. For that setting, Yao and colleagues, building the tau-bench benchmark for tool-using agents interacting with simulated customers under domain policies, proposed the complementary statistic: the probability that every one of k independent trials on the same task succeeds, rather than at least one of them [ 3 ] .
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