请问HN：在隔离环境中运行AI代理
嗨，HN，
这个问题是受到这篇帖子启发的：https://news.ycombinator.com/item?id=44196417。我注意到最近在讨论中这个话题经常出现。
到目前为止，我看到的大多数项目似乎更像是概念验证实验——考虑到事物发展的速度，这也是可以理解的。尽管我很想采用这些工具，但它们往往会引入对单人项目的依赖，这对于我的大型项目来说风险太大。在事情稍微稳定之前，我更倾向于建立一个自定义流程，以便更好地控制环境。
考虑到这一点，我很好奇你们在项目中是如何处理这些问题的。具体来说：
- 你们使用什么方法来隔离AI代理？（例如，基于容器的解决方案如Docker/Kubernetes与基于云的解决方案）
- 如果你们使用云服务：有没有好的替代GitHub Codespaces的选择，能够减少供应商锁定，但仍然易于管理？
- 你们如何在简单设置与团队协作的需求之间取得平衡？
- 你们如何处理AI代理可能使用或生成的大型数据库或数据集（例如，存储、访问、性能）？
我非常希望听到你们的见解、最佳实践或遇到的任何陷阱！
谢谢！
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Hi HN,<p>This question was triggered by this post: https://news.ycombinator.com/item?id=44196417. I’ve noticed that this topic comes up a lot in discussions lately.<p>Most of the projects I’ve seen so far seem to be more like proof-of-concept experiments—understandable, given the pace at which things are moving. As much as I’d like to adopt such tools, they often introduce dependencies on single-person projects, which feels too risky for my larger projects. Until things stabilize a bit, I’d rather set up a custom flow that gives me more control over the environment.<p>With that in mind, I’m curious how you approach this in your projects. Specifically:<p>- What approaches do you use to isolate AI agents? (e.g. container-based solutions like Docker/Kubernetes vs. cloud-based solutions)<p>- If you’re using the cloud: Are there good alternatives to GitHub Codespaces that offer less vendor lock-in but are still easy to manage?<p>- How do you balance a simple setup with the need for team collaboration?<p>- How do you handle large databases or datasets that AI agents might use or generate (e.g. storage, access, performance)?<p>I’d really appreciate hearing your insights, best practices, or any pitfalls you’ve encountered!<p>Thanks!