Stop Counting PRs. Measure the Queue.

说明与用途

从计数 PR 转向衡量队列：审核等待时间、进入 PR、批量大小、首次通过率、返工率、控制覆盖率和已接受更改成本，以评估真实生产力。

问题

Teams using AI agents generate code faster than they can review it, leading to inflated PR counts that do not reflect true productivity.

结果

By shifting focus from PR counts to queue metrics, teams can identify review bottlenecks, ensure quality of agent output, and align productivity with actual delivery outcomes.

工作流

• Track time from PR creation to first human review.
• Record incoming PRs and PR size each week.
• Monitor batch size to detect increasing review complexity.
• Measure first-pass acceptance and rework rate of agent-authored code.
• Track harness coverage for known failure modes.
• Calculate cost per accepted change including compute, tokens, and review time.
• Compare queue metrics against DORA metrics (deployment frequency, lead time, change failure rate, time to restore).
• Adjust review capacity or agent output based on queue growth patterns.

证据

AI agents can generate code faster than your team can review it.

来源

Instead of counting pull requests alone, track: • Review wait time: Time from PR creation to first human review.

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Track a few additional metrics: • First-pass acceptance: How often agent changes are accepted without human edits.

来源

Keep the four DORA metrics, too: deployment frequency, lead time for changes, change failure rate, and time to restore service.

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