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2026-05-06 2 浏览 免费阅读

趋势解读:Vibe coding and agentic engineering are getting closer,评估 LLM Agent 表现

Simon Willison 在播客中分享了他对 vibe coding 和 agentic engineering 融合的观察,以及 AI 生成代码评估的新挑战。

SOURCE / AI技能杠杆 MIN / 4 ACCESS / 免费阅读 POST / 2026-05-06 22:24:08

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作者:Simon Willison 来源站点:simonwillison.net 原贴时间:
趋势解读:Vibe coding and agentic engineering are getting closer,评估 LLM Agent 表现

原文

I recently talked with Joseph Ruscio about AI coding tools for Heavybit's High Leverage podcast: Ep. #9, The AI Coding Paradigm Shift with Simon Willison . Here are some of my highlights, including my disturbing realization that vibe coding and agentic engineering have started to converge in my own work. One thing I really enjoy about podcasts is that they sometimes push me to think out loud in a way that exposes an idea I've not previously been able to put into words. Vibe coding and agentic engineering are starting to overlap A few weeks after vibe coding was first coined I published Not all AI-assisted programming is vibe coding (but vibe coding rocks) , where I firmly staked out my belief that "vibe coding" is a very different beast from responsible use of AI to write code, which I've since started to call agentic engineering . When Joseph brought up the distinction between the two I had a sudden realization that they're not nearly as distinct for me as they used to be: Weirdly though, those things have started to blur for me already, which is quite upsetting. I thought we had a very clear delineation where vibe coding is the thing where you're not looking at the code at all. You might not even know how to program. You might be a non-programmer who asks for a thing, and gets a thing, and if the thing works, then great! And if it doesn't, you tell it that it doesn't work and cross your fingers. But at no point are you really caring about the code quality or any of those additional constraints. And my take on vibe coding was that it's fantastic, provided you understand when it can be used and when it can't. A personal tool for you, where if there's a bug it hurts only you, go ahead! If you're building software for other people, vibe coding is grossly irresponsible because it's other people's information. Other people get hurt by your stupid bugs. You need to have a higher level than that. This contrasts with agentic engineering where you are a professional software engineer. You understand security and maintainability and operations and performance and so forth. You're using these tools to the highest of your own ability. I'm finding the scope of challenges I can take on has gone up by a significant amount because I've got the support of these tools. But I'm still leaning on my 25 years of experience as a software engineer. The goal is to build high quality production systems: if you're building lower quality stuff faster, I think that's bad. I want to build higher quality stuff faster. I want everything I'm building to be better in every way than it was before. The problem is that as the coding agents get more reliable, I'm not reviewing every line of code that they write anymore, even for my production level stuff. I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it's just going to do it right. It's not going to mess that up. You have it add automated tests, you have it add documentation, you know it's going to be good. But I'm not reviewing that code. And now I've got that feeling of guilt: if I haven't reviewed the code, is it really responsible for me to use this in production? The thing that really helps me is thinking back to when I've worked at larger organizations where I've been an engineering manager. Other teams are building software that my team depends on. If another team hands over something and says, "hey, this is the image resize service, here's how to use it to resize your images"... I'm not going to go and read every line of code that they wrote. I'm going to look at their documentation and I'm going to use it to resize some images. And then I'm going to start shipping my own features. And if I start running into problems where the image resizer thing appears to have bugs or the performance isn't good, that's when I might dig into their Git repositories and see what's going on. But for the most part I treat that as a semi-black box that I don't look at until I need to. I'm starting to treat the agents in the same way. And it still feels uncomfortable, because human beings are accountable for what they do. A team can build a reputation. I can say "I trust that team over there. They built good software in the past. They're not going to build something rubbish because that affects their professional reputations." Claude Code does not have a professional reputation! It can't take accountability for what it's done. But it's been proving itself anyway - time and time again it's churning out straightforward things and doing them right in the style that I like. There's an element of the normalization of deviance here - every time a model turns out to have written the right code without me monitoring it closely there's a risk that I'll trust it at the wrong moment in the future and get burned. The new challenge of evaluating software It used to be if you found a GitHub repository with a hundred commits and a good readme and automated tests and stuff, you could be pretty sure that the person writing that had put a lot of care and attention into that project. And now I can knock out a git repository with a hundred commits and a beautiful readme and comprehensive tests of every line of code in half an hour! It looks identical to those projects that have had a great deal of care and attention. Maybe it is as good as them. I don't know. I can't tell from looking at it. Even for my own projects, I can't tell. So I realized what I value more than the quality of the tests and documentation is that I want somebody to have used the thing. If you've got a vibe coded thing which you have used every day for the past two weeks, that's much more valuable to me than something that you've just spat out and hardly even exercised. The bottlenecks have shifted If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code. And now it doesn't. It's not just the downstream stuff, it's the upstream stuff as well. I saw a great talk by Jenny Wen , who's the design leader at Anthropic, where she said we have all of these design processes that are based around the idea that you need to get the design right - because if you hand it off to the engineers and they spend three months building the wrong thing, that's catastrophic. There's this whole very extensive design process that you put in place because that design results in expensive work. But if it doesn't take three months to build, maybe the design process can be a whole lot riskier because cost, if you get something wrong, has been reduced so much. Why I'm still not afraid for my career When I look at my conversations with the agents, it's very clear to me that this is moon language for the vast majority of human beings. There are a whole bunch of reasons I'm not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience. If you know what you're doing, you can run so much faster with them. [...] I'm constantly reminded as I work with these tools how hard the thing that we do is. Producing software is a ferociously difficult thing to do. And you could give me all of the AI tools in the world and what we're trying to achieve here is still really difficult. [...] Matthew Yglesias, who's a political commentator, yesterday tweeted , "Five months in, I think I've decided that I don't want to vibecode — I want professionally managed software companies to use AI coding assistance to make more/better/cheaper software products that they sell to me for money." And that feels about right to me. I can plumb my house if I watch enough YouTube videos on plumbing. I would rather hire a plumber. On the threat to SaaS providers of companies rolling their own solutions instead: I just realized it's the thing I said earlier about how I only want to use your side project if you've used it for a few weeks. The enterprise version of that is I don't want a CRM unless at least two other giant enterprises have successfully used that CRM for six months. [...] You want solutions that are proven to work before you take a risk on them. Tags: ai , generative-ai , llms , podcast-appearances , vibe-coding , coding-agents , agentic-engineering

中文翻译

我最近与 Joseph Ruscio 在 Heavybit 的 High Leverage 播客中讨论了 AI 编程工具:第 9 集,与 Simon Willison 一起探讨 AI 编程范式转变。以下是我的一些亮点,包括我令人不安的发现,即 vibe coding 和 agentic engineering 已经开始在我的工作中融合。我非常喜欢播客的一点是,它们有时会促使我进行口头思考,从而暴露出我此前无法用语言表达的想法。

Vibe coding 和 agentic engineering 开始重叠。在 vibe coding 被首次提出几周后,我发表了《并非所有 AI 辅助编程都是 vibe coding(但 vibe coding 很酷)》,在其中我坚定地表明了我的信念:vibe coding 与负责任地使用 AI 编写代码是截然不同的,后者我开始称之为 agentic engineering。当 Joseph 提到两者之间的区别时,我突然意识到,对我来说它们已经不像以前那样界限分明了:奇怪的是,这些东西对我来说已经开始模糊,这相当令人不安。我曾认为我们有非常清晰的划分:vibe coding 是那种你根本不看代码的情况。你可能甚至不知道如何编程。你可能是一个非程序员,你要求一个东西,然后得到一个东西,如果这个东西能用,那就太好了!如果不行,你就告诉它不行,然后祈祷。但你在任何时候都不真正关心代码质量或任何其他约束。我对 vibe coding 的看法是,这很棒,前提是你知道何时可以使用以及何时不能。一个你个人的工具,如果有一个 bug 只会影响你,那就去做吧!但如果你正在为他人构建软件,vibe coding 是极其不负责任的,因为涉及他人的信息。别人会因为你的愚蠢 bug 而受到伤害。你需要有比那更高的标准。这与 agentic engineering 形成对比,在 agentic engineering 中你是一名专业软件工程师。你了解安全性、可维护性、运维和性能等。你正在利用自己的最高能力使用这些工具。我发现我能应对的挑战范围显著扩大了,因为有了这些工具的帮助。但我仍然依赖我 25 年的软件工程师经验。目标是构建高质量的生产系统:如果你更快地构建低质量的东西,我认为那很糟糕。我想更快地构建更高质量的东西。我希望我构建的一切都在各个方面比以前更好。问题是,随着编码代理变得更加可靠,我不再审查它们编写的每一行代码,即使是在我的生产级代码中。我很清楚,如果你让 Claude Code 构建一个运行 SQL 查询并将结果输出为 JSON 的 JSON API 端点,它会正确地完成。它不会搞砸。你让它添加自动化测试,你让它添加文档,你知道它会是好的。但我并没有审查那部分代码。现在我有了一种内疚感:如果我没有审查代码,我真的能在生产中使用它吗?真正帮助我的是回想起我在大型组织担任工程经理的经历。其他团队正在构建我的团队所依赖的软件。如果另一个团队交出一个东西说:“嘿,这是图片缩放服务,这是如何使用它来缩放你的图片的文档。”我不会去阅读他们写的每一行代码。我会查看他们的文档,然后使用它来缩放一些图片。然后我会开始发布自己的功能。如果我开始遇到问题,比如图片缩放器似乎有 bug 或者性能不好,那时我可能会深入他们的 Git 仓库看看发生了什么。但大多数情况下,我把它当作一个半黑箱,直到需要时才去看。我开始以同样的方式对待这些代理。这仍然让我感到不舒服,因为人类要为自己的行为负责。一个团队可以建立声誉。我可以说“我相信那个团队。他们过去构建了好的软件。他们不会构建垃圾,因为这会影响他们的职业声誉。”Claude Code 没有职业声誉!它不能为自己的行为负责。但它一直在证明自己——一次又一次地,它生成直截了当的代码,并以我喜欢的方式正确完成。这里有偏差正常化的问题——每次模型没有在我严密监控的情况下写出正确的代码,都有一种风险,即我将来会在错误的时刻信任它并遭受损失。

评估软件的新挑战。过去,如果你找到一个 GitHub 仓库,有一百次提交、一份好的 README 和自动化测试等,你很可能相信编写者投入了大量心血和关注。而现在,我可以在半小时内创建一个有一百次提交、漂亮的 README 和每行代码的全面测试的 Git 仓库!它看起来与那些投入大量心血和关注的项目一模一样。也许它和它们一样好。我不知道。我无法从外观上判断。即使是我自己的项目,我也无法判断。所以我意识到,我比测试和文档质量更看重的是有人真正用过这个东西。如果你有一个 vibe coding 出来的东西,并且你在过去两周每天都使用它,那对我来说比一个你刚吐出来几乎未经测试的东西有价值得多。

瓶颈已经转移。如果你能从每天生成 200 行代码变成每天 2000 行代码,还有什么会出问题?整个软件开发生命周期,事实证明,

核心信息

Simon Willison 在播客中分享了他对 vibe coding 和 agentic engineering 融合的观察,以及 AI 生成代码评估的新挑战。

  • vibe coding 与 agentic engineering 界限模糊
  • AI 代理可靠性提升导致代码审查减少
  • 传统代码质量评估指标失效
  • 真实使用验证比测试文档更重要
  • 开发者需警惕偏差正常化风险

详细解读

这是什么信号? Simon Willison 指出,随着 AI 编码代理(如 Claude Code)可靠性的提升,专业开发者开始减少对生成代码的审查,导致专业工程实践(agentic engineering)与“无脑编码”(vibe coding)的界限逐渐模糊。这是一个危险的信号:依赖声誉和历史的行业准则正在被 AI 的“无责任”输出所侵蚀,评估代码质量的传统指标(如提交次数、测试覆盖率)变得不可靠。

为什么重要? 传统软件工程依赖开发者对代码的深度理解和责任心,而 AI 代理虽能高效产出,却无法承担后果。当开发者将代理视为可信赖的“黑箱”时,潜在风险(如安全漏洞、性能问题)可能被忽视,直到故障发生。这改变了软件评估的本质:从审查代码变为审查使用者的验证过程。Willison 强调,真正的质量指标不再是代码本身,而是产品是否被实际使用和验证。

对谁有价值? 对 AI 工具开发者、专业软件工程师、技术管理者以及依赖 AI 编码的初创团队。工程师需重新思考代码审查策略;管理者需建立新的质量保障流程;创业者可利用 AI 加速原型,但必须警惕生产环境中的隐蔽风险。

可以怎么行动? 1)建立“验证优先”的流程:要求 AI 生成的代码必须通过实际使用测试,而非仅依赖测试套件。2)划分安全边界:将 AI 代码用于低风险、内部工具或快速原型,对关键系统保持人工审查。3)培养“评估直觉”:通过持续监控 AI 输出模式,积累信任阈值,但始终保留回退机制。

风险或限制 核心风险是“偏差正常化”:随着 AI 频繁表现良好,开发者会逐渐降低警惕,可能在关键时刻因过度信任而犯错。此外,AI 缺乏责任归属,无法像人类团队那样通过声誉机制建立长期信任。当前 AI 对复杂业务逻辑、安全敏感场景的可靠性仍有上限,需谨慎对待。

信息差价值

信息差价值: Simon Willison 作为知名开发者,首次公开承认自己开始像信任人类团队一样信任 AI 代理,这一心态转变反映了行业前沿的真实实践。他提出的“使用验证优于代码审查”观点,打破了主流对 AI 编程的乐观叙事,揭示了深层风险。

业务启发: 对于 AI 工具公司,这暗示着用户需求已从“生成代码”转向“可靠验证”——可以设计内置使用统计、自动异常检测的功能。对于软件团队,需重新分配资源:减少静态代码审查,增加集成测试和用户验收测试的投入。

可沉淀动作: 1)为每个 AI 生成的组件建立“使用日志”,记录其在生产中的实际表现。2)对 AI 代码实施“渐进式放权”:先在小范围使用,观察一段时间后酌情扩大。3)制定“代理信任等级”制度,根据任务风险确定人工审查强度。

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趋势解读:Vibe coding and agentic engineering are getting closer,评估 LLM Agent 表现主要讲什么?

Simon Willison 在播客中分享了他对 vibe coding 和 agentic engineering 融合的观察,以及 AI 生成代码评估的新挑战。

这篇文章最值得关注的要点是什么?

Simon Willison 在播客中分享了他对 vibe coding 和 agentic engineering 融合的观察,以及 AI 生成代码评估的新挑战。;vibe coding 与 agentic engineering 界限模糊;AI 代理可靠性提升导致代码审查减少;传统代码质量评估指标失效

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它适合放在Agent工作流、AI工具、AI超级个体专题里阅读。 关联原因:这篇内容命中「Agent、工作流、AI编程」等主题信号。;这篇内容命中「自动化」等主题信号。;这篇内容命中「技能」等主题信号。

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