Every AI agent project starts the same way. You create an index.ts , add a prompt, maybe define a couple of tools, and everything works. For a while, it even feels clean and manageable. Then the system starts to grow. You introduce memory, add logging, experiment with multiple agents, and eventually build workflows. At that point, the simplicity disappears and the codebase turns into a collection of loosely connected files with no clear structure.

This is the part most tutorials skip. They show how to call a model, but they rarely show how to organize a system around it.

In a previous article, I discussed why AI agents should be designed as controlled systems where the model proposes actions and the application owns validation, execution, and safety. This article is the practical extension of that idea.…