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How to Measure AI Agent Cost Per Resolved Task, Not Per API Call

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A software developer has outlined a practical framework for tracking the true unit economics of AI agent features, arguing that cost-per-API-call is a misleading metric. The correct measure, they explain, is cost per resolved task — calculated by dividing total run costs, including failed attempts, by the number of successfully completed tasks. Key optimizations identified include prompt caching with stable system-prompt prefixes, reducing agent turn count, routing tasks to cheaper models by phase, and using high-threshold semantic caching to skip model calls entirely. The author also highlights that cost distributions are long-tailed, meaning averages can be deceptive, and that segmenting costs by user intent often reveals which feature areas are driving the most spend. A Budget class and SQL query template are provided to help engineering teams instrument and analyze these metrics in production.

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How to Measure AI Agent Cost Per Resolved Task, Not Per API Call · ShortSingh