The Three-File Stack: How to Stop AI Agents from Drifting

The Three-File Stack: How to Stop AI Agents from Drifting Most AI agent reliability problems aren't capability problems. They're identity and memory problems. An agent that can't remember what it's supposed to be — or what it was doing — will drift. Subtly at first, then catastrophically. Here's the simplest pattern I've found to fix it: three files, reloaded every turn . The Problem: Agents Forget Who They Are Large language models don't have persistent state. Every time your agent takes a turn, it starts fresh from its context window. If you don't actively reload identity, memory, and task state — the agent will revert toward generic LLM behavior. It'll hallucinate. It'll forget its constraints. It'll answer questions it should be routing elsewhere. This isn't a bug you can fix with a better model. It's a structural problem. And the structural fix is simple. The Three-File Stack 1. SOUL.md — Identity This file defines who the agent is. Role, tone, values, escalation rules, what it sh