We let AI agents write 1,450 commits of our product. Here’s what code review became.

说明与用途

在 Deckgauge 中引入 AI 代理后，代码审查从逐行检查转为基于意图的检查，并通过严格的 CI 门控确保只有通过所有自动化检查的提交才会进入人工审查，从而减少审查时间并捕获细微逻辑错误。

问题

Integrating AI coding agents into a measurement product risked introducing subtle, plausible bugs that traditional code review would miss.

结果

By enforcing gates before human review and focusing on data assumptions, Deckgauge reduced review time, increased confidence in metric correctness, and avoided subtle AI‑generated bugs.

工作流

• 1. AI agent generates code and commits with co‑author trailer.
• 2. Automated pipeline runs lint, build, type‑check, and test suite; commit is blocked if any fail.
• 3. Human reviewer receives only green commits.
• 4. Reviewer asks three questions: (a) What data assumptions are made? (b) What happens at data boundaries? (c) Does the output match intended metric?
• 5. Reviewer approves or requests changes; changes are made by developer or AI as needed.

证据

We stopped reading diffs line by line and started reading them assumption by assumption.

来源

The discipline that actually made this work: no agent task reaches a human until it has passed lint, build, typecheck, and the test suite.

来源