Lauren 认为管理 AI 智能体的经验来自优秀程序员与计算机科学家,代码库中的 lint 规则、更智能的编译器与诊断、高质量测试和可观测性等约束能同时解放人类与智能体。过去大公司用这些手段解决工程师被迫跨领域写代码导致的低质量问题,智能体出现后,这类问题已成为所有开发团队都要面对的问题,但解法仍是成熟的良好工程实践。
everything i know about managing agents i learned from the amazing programmers and computer scientists that came before
constraints in your codebase are freeing for both humans and agents! this has always been true, but if you were a small dev team you likely never had to think that deeply because you could trust your fellow programmers to write code and review well
but large companies have had to solve this problem since even before agents. because before you had agent slop, you had human slop. lots of engineers wrote bad code out of necessity (eg backend engineer forced to write frontend or reverse). the solution to this was constraints: lint rules, smarter compilers and diagnostics, high quality tests, investments into observability, and so on
the arrival of agents just means that big company problems are now everyone's problems. the good news is that none of this is really that novel: it's just good engineering
https://www.youtube.com/watch?v=GqmsQeSzMdw
来源:lauren · x.com