HackerNews中文版

无论是在本地部署还是托管的 AI 安排中，我都看到很多人发帖表示他们需要 24/7 的智能体（agent），尤其是在软件领域。这些智能体到底在做什么？这些模型离前沿技术有多近，又需要多少成本？我经营一家 SaaS 公司，但我感觉智能体并没有真正为我工作，因为我分配给它们一天的最后一个大型任务完成后，它们就停止了。我有一个巨大的待办事项积压，希望能自动化处理，但我很难理解人们如何从任务中获得高质量的输出，用户应该在哪个环节介入，以及事物如何从想法到任务，再到自动化构建，最终到我认为的最后人工干预——审查过程。总的来说，我假设他们使用某种问题跟踪系统，如 Linear 或 GitHub Issues，然后连接到云端智能体或工作树，在那里进行构建和测试？对于那些不是基于 bug 或问题，而是实际功能的需求，又该如何处理？您提供多少上下文？如何阻止 AI 在需要时自行其是，而不是寻求澄清？这种设置真的有净收益吗？还是说，大多数时候您最终不得不修补在夜间自主完成的所有工作？

查看原文

In either a local AI or a hosted AI arrangements, I'm seeing lots of posts of people saying they have a need for agents 24/7, assuming they're in the software space. What are these agents actually doing? How close to the frontier are these models, and what does it cost?<p>I run a SaaS business, but I feel like agents aren't really working for me as once the last large task finishes that I assign for the day. I have a huge backlog that I would like to automate but I'm struggling to comprehend how people get quality outputs from tasks and where the user is supposed to be in the pipeline and how things go from idea to task to automated build to what I assume is the final human intervention which is the review process.<p>Top line, I'm assuming they're using some sort of issue tracking system like Linear or GitHub issues into either a cloud agent or a work tree where it's built out and tested? What about for things that aren't bug or issue based, but are actually features? How much context do you provide? How do you stop the AI just running with things instead of asking for clarity when required, etc.<p>Is there actually a net positive benefit to this setup, or do you end up just having to patch all of the work that was done autonomously overnight most of the time?

HN 提问：各位 24/7 运行代理（agent）的朋友们，你们的工作流程是怎样的？它们在做什么？