What Metrics Actually Prove AI Coding Agents Are Working?

AI coding agents should be evaluated through a combination of autonomy rate, cost per merged pull request, defect or rollback rate, human intervention rate, and cycle time, all compared with a meaningful pre-agent baseline rather than seat counts or raw PR volume. High output alone can conceal review bottlenecks, inflated activity, or quality problems, while DORA metrics such as lead time and change failure rate remain useful when interpreted carefully, whereas time to restore service is less relevant because incident response is still largely human-led. Reliable attribution requires session-level records showing how much of a final change originated with an agent versus human revisions, since commit history alone cannot establish authorship. Performance should be monitored continuously as models, team practices, and reviewer trust evolve, beginning with a narrowly defined workflow before expanding measurement. Warp Factories is presented as a platform that provides throughput, cost, and quality metrics, customizable scoring, and automated feedback loops that can adjust factory configurations when performance declines.

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