Originally published on hexisteme notes.
If you use AI agents daily, you have hit this: the agent gives a correct, well-reasoned answer on Monday. On Friday, in a fresh session, it gives a different answer to the same question. Not wrong — just different, shaped by whatever context happened to come up first.
That is drift. And it is not fixable with better prompts or more examples piled into a CLAUDE.md file. Those approaches scale poorly and they do not version. What actually works is treating each repeatable decision as a small, versioned artifact I call a Reusable Decision Unit (RDU).
The one-sentence version: encode each repeatable decision as a versioned markdown file with a grep-able trigger and a committed judgment, store it where the agent loads it at session start, and the rule fires automatically on trigger instead of relying on the model's memory. I run a library of 140+ of these across iOS deployment, trading, AI-agent design, game development, and real estate. This is how they are built and why they hold.
The problem with "just write it down"
