循环工程：让 AI 负责执行，让人类聚焦决策

去年开发者是 AI 编码代理的 QA——手动找 bug，手动让代理修， 今年代理能自己测自己修了， 吴恩达老师管这叫"循环工程"， 但我觉得真正值得说的不是这个循环工程本身， 上周末他给女儿做了一个打字练习 app，编码代理自己跑了一小时， 用浏览器反复检查自己写的东西， 没要他干预。 他要做的不是检查代码，是决策，比如视觉设计怎么调、猫咪皮肤加几个、家长登录流程怎么改。 以前这些东西藏在"有空再优化"列表里，现在代理把代码层的事吃了，决策层的事就全浮出来了。 吴恩达用了一个词来形容——叫"语境优势"。 他说很多人把人类在循环里的价值叫"品味"，他不喜欢这个词， 因为品味听起来像玄学，人类真正的优势不是品味， 是语境——你知道用户是谁、为什么痛苦、什么功能他们会疯传。 这些事代理不知道，不是因为模型不够强，是因为这些信息不在训练数据里。 循环工程真正的洞察在这：它可以加速代码，但不能压缩语境。 只要人拥有代理没有的信息，人就永远在循环里有一层不可替代的位置。 只不过这层位置一直在往上移，从 QA 移到 PM，从检查移到判断。 我觉得最容易被取代的，是代理能自己测的那部分工作，而回不去的是那种只有你知道用户想要什么的那一部分工作。 所以循环工程真正的意义，不是让 AI 跑得更久，其实是反向逼你的能力不断往上走 https://video.twimg.com/amplify_video/2072023960632410112/vid/avc1/1264x720/o3nODqAhklEpDvuk.mp4?tag=25 > **引用原帖 Andrew Ng (@AndrewYNg):** > “Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. > Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. > The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! > Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. > The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. > When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. > AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. > External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. > With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! > I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). > [Original text: The Batch] > https://x.com/AndrewYNg/status/2071988145667928442 AYi (@AYi_AInotes): 越想越觉得，循环工程把人推到的那个更高楼层，其实才是产品/工程最值钱的部分，AI 把执行 commodity 化了，人的决策和判断反而更稀缺了