We’ve heard about prompt engineering and then context engineering, and now it’s loop engineering. Googler Addy Osmani offers a clear introduction to it, beginning with a simple definition:

Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of a recursive goal where you define a purpose and the AI iterates until complete.

What changes is who keeps the work moving:

For like two years the way you got something out of a coding agent was you wrote a good prompt and shared enough context. You type a thing, you read what came back, you type the next thing. The agent is a tool and you are holding it the entire time, one turn after the other. That part is kind of over, or at least some think it’s going to be.

Now you build a small system that finds the work, hands it out, checks it, writes down what is done and then decides the next thing, and you let that system poke the agents instead of you. I wrote before about the cousin of this, agent harness engineering, which is making the environment one single agent runs inside and the factory model - the system that builds the software. Loop engineering sits one floor above the harness. The harness but it runs on a timer, it spawns little helpers, and it feeds itself.

And the loop itself has a recognizable anatomy:

A loop needs five things and then one place to remember stuff. Let me list it first and then map it.

1. Automations that go off on a schedule and do discovery and triage by themselves.
2. Worktrees so two agents working in paralell dont step on each other.
3. Skills to write down the project knowledge the agent would otherwise just guess.
4. Plugins and connectors to plug the agent into the tools you already use.
5. Sub-agents so one of them has the idea and a different one checks it.

Then the sixth thing, the memory. A markdown file, or a Linear board, anything that lives outside the single conversation and holds what’s done and what is next. Sounds too dumb to matter. But it’s the same trick every long running agent depends on and I went into it in long-running agents, the model forgets everything between runs so the memory has to be on disk and not in the context. The agent forgets, the repo doesnt.