问HN：那些全天候运行代理的人，你们的工作流程是怎样的？它们在做什么？
在本地AI或托管AI的安排中，我看到很多人发帖表示他们需要24/7的代理，假设他们是在软件领域。那么这些代理实际上在做什么？这些模型离前沿技术有多近，成本又是多少？
我经营一家SaaS业务，但我觉得代理对我并没有真正起作用，因为一旦我分配的最后一个大型任务完成后，代理就没有什么用处了。我有一个巨大的待办事项清单，希望能够自动化，但我很难理解人们是如何从任务中获得高质量输出的，用户在整个流程中应该处于什么位置，以及从创意到任务再到自动构建，最后我假设是人工干预的审核过程是如何进行的。
总的来说，我假设他们使用某种问题跟踪系统，比如Linear或GitHub Issues，将其转入云代理或工作树中进行构建和测试。那么对于那些不是基于错误或问题的，而是实际功能的任务呢？你提供了多少上下文？你如何防止AI在需要澄清时不询问而是自行处理？
这种设置实际上是否带来了净正收益，还是大多数时候你最终只能修补那些在夜间自动完成的工作？
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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?