当前构建 AI Agent 的过程基本是这样： 为了解决模型的某项限制，你需要搭建各种“脚手架”结构，让你的 Agent 能正常工作。 随后模型被升级，直接解决了你当初费力绕开的那个问题，结果你的脚手架瞬间变得过时。 你再找到一个更难的新用例想去解决，然后回到第 1 步重新开始。 这个循环几乎无法避免。 因为如果你不补上模型的短板，你的产品一开始就会死掉； 但你也无法预知模型会在什么时候、在哪方面超越你写的这些补丁。 现实情况就是：接下来的几年里，你必须接受自己会写掉大量“一次性代码”，并且要对自己走过的路线保持绝对冷静，不要执念。 核心目标只有一个：让 Agent 跑起来，不惜一切代价。 Building AI agents right now is a process of: 1. Build scaffolding to address a limitation of the AI model so your agent works 2. The AI model gets upgraded and solves the very problem you were trying to mitigate, rendering your scaffolding obsolete 3. Identify new, harder use case you want to solve and go back to step 1 There’s basically no way to avoid this process because if you don’t mitigate the model’s limitations then you’re dead on arrival, *and* you don’t know which of your mitigations will be surpassed and when. So the reality is that you just have to accept that you are going to be writing a lot of throwaway code for the next few years, and you have to be very unsentimental to the path you’ve pursued. Just do whatever it takes to make the agents work.