Loop Engineering

There's a moment you've probably hit with an AI tool. You ask it to do something with a few moving parts - clean up a spreadsheet, draft and send a sequence of emails, fix a thing in a codebase - and it produces an answer that looks finished, sounds confident, and is wrong in a way it never noticed. It didn't lie to you. It took one swing and called the game.

That gap is what this guide is about. The fix isn't a better prompt or a smarter model. It's giving the AI a loop: do something, check the result, adjust, and go again until the result actually holds up. People have started calling the craft of designing that loop "loop engineering." It's a newer term, not fully standardized, and we'll be honest about that throughout - but the underlying idea is real and it's the thing that separates an agent that finishes wrong from one that grinds toward right.

This guide is for the people steering these tools, not building them - founders, operators, writers, anyone handing real work to an AI agent and wondering why it sometimes face-plants on tasks that should be within reach. No math, no model internals. Phase 1 makes the case that the loop, not the single answer, is the unit of real work, and why one-shot prompting falls short. Phase 2 gets practical: what makes a loop good - verifiable goals, clear stop conditions, and feedback the agent can actually act on. Phase 3 steps back and treats the term itself honestly: where it came from, how it sits next to prompt and context engineering, and what's still being argued over. By the end you'll know how to set up work so the AI catches its own mistakes instead of handing them to you.