问HN：以代理驱动的质量保证（QA）是否存在？
TDD（测试驱动开发）已经成为AI编码代理的默认工作流程，原因有很多。<p>我最近开始做的一件我非常喜欢的事情是，在构建一个功能时，我会编写验收标准，然后确保代理拥有完成质量保证（QA）和自行验证所需的一切。与之前最大的不同在于，我不再自己进行质量保证（虽然我仍然会做，但几乎总是有效的），而是让代理能够自行执行整个流程并验证验收标准。这需要更多的工作，因为我通常需要设置MCP（最小可行产品）、账户、凭证等，但最终的输出效果更好。<p>我的问题是……是否有人对此有好的方法？这是一个常见的工作流程吗？我看到一些关于这个话题的讨论称为“工具工程”；有些称为“反馈循环工程”；还有一些初创公司（如Shiplight AI、Autosana、Ranger）。我想找到更多的讨论和最佳实践，看看其他人是如何思考这个问题的。
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TDD has become the default workflow for AI coding agents, for a lot of reasons.<p>Something I recently started doing that I really like is, when building a feature, I write acceptance criteria and then I make sure the agents have everything they need to do QA and verify it themselves. The main difference from before is that instead of QAing it myself (which I still do but it's almost always working), the agent can exercise the whole flow itself and verify the acceptance criteria. It takes more work because I often have to set up MCPs, accounts, credentials, etc - but the output is better.<p>My question... do people have good approaches to this? Is this a common workflow? I see some of this conversation called harness engineering; I see some called feedback-loop engineering; I see some startups (Shiplight AI, Autosana, Ranger). I'm trying to find more discussion & best practices here, and see how others are thinking about this.