How We Run Lullwood With Eleven Claude Agents and One Release Train

说明与用途

Lullwood 使用 11 名 AI 代理在共享仓库上工作。将阻塞式审查限制在高风险更改后，团队减少了审查时间并将发布速度翻倍。

问题

Reviewing every change in a multi‑agent development environment consumed more effort than creating new features, limiting release velocity.

结果

By limiting blocking reviews to high‑risk changes, the team reduced review overhead, doubled shipping throughput, and maintained acceptable defect rates.

工作流

• 1. Agents submit pull requests to the shared repo.
• 2. Automated checks run on all PRs.
• 3. High‑risk PRs are routed to a Code Reviewer for blocking review.
• 4. Low‑risk PRs merge automatically after checks pass.
• 5. Release train incorporates merged changes and deploys to Vercel.

证据

In one week the two agents building the game ran 803 sessions between them, and the two agents gating that work ran 853.

来源

Docs, tests and copy ship the moment checks are green, with no review.

来源

The engine's core simulation, persistence, anything touching secrets or merge rules, and release cuts still need a blocking review before they land.

来源