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为了提高 AI 智能体的效率，我们需要构建与真实系统之间的反馈循环：部署、日志、配置、环境、仪表盘。但问题就出在这里。大多数现代应用程序没有细粒度的权限控制。举个具体的例子：Vercel。如果我想让一个智能体读取日志或检查环境变量，我必须给它一个令牌，而这个令牌也允许它修改或删除东西。没有干净的只读或能力范围限定的访问权限。这不仅仅是 Vercel 的问题。我在云仪表盘、CI/CD 系统和 SaaS API 中也看到了同样的模式，这些系统都是围绕着值得信赖的人类设计的，而不是自主智能体。所以真正的问题是：人们今天在生产环境中是如何限制 AI 智能体的？你们是在构建强制执行策略的代理层吗？用白名单封装 API 吗？还是仅仅接受风险？感觉我们正在试图将自主系统连接到从未为它们设计的基础设施上。很想知道其他人是如何在实际环境中处理这个问题的，而不是停留在理论层面。

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To make AI agents more efficient, we need to build feedback loops with real systems: deployments, logs, configs, environments, dashboards.But this is where things break down.Most modern apps don’t have fine-grained permissions.Concrete example: Vercel. If I want an agent to read logs or inspect env vars, I have to give it a token that also allows it to modify or delete things. There’s no clean read-only or capability-scoped access.And this isn’t just Vercel. I see the same pattern across cloud dashboards, CI/CD systems, and SaaS APIs that were designed around trusted humans, not autonomous agents.So the real question:How are people actually restricting AI agents in production today?Are you building proxy layers that enforce policy? Wrapping APIs with allowlists? Or just accepting the risk?It feels like we’re trying to connect autonomous systems to infrastructure that was never designed for them.Curious how others are handling this in real setups, not theory.

Ask HN: 如何在不赋予过多权限的情况下，让 AI 智能体访问资源？