Loop Engineering Explained Visually
Loop Engineering
LOOP Engineering
For two years, the standard AI workflow looked the same regardless of what you were building. Open a chat, type a request, get output, review it, type the next request. You were running the loop manually. The model executed one step and waited.
That rhythm made sense when models were unreliable. A human checkpoint at every step caught errors before they compounded. The problem is that’s no longer the constraint. The models got better. The prompting pattern didn’t change.
Agent looping replaces the manual checkpoint with an automated one.
The One-Task Problem
Every time you prompt an agent for the next step, you’re making a decision the agent should be making. Where to look. Whether the draft is good enough. What still needs work. The prompt-and-review cycle keeps you in the middle of tasks that don’t need you there.
Think of hiring a writer and calling them after every paragraph to ask what to write next. You get results, but you’re running the operation. The “automation” is expensive autocomplete with extra steps.
The fix isn’t a better model. It’s changing how you wire the agent.
What a Loop Actually Is
A single agent loop works like this. One agent handles a repeating cycle: research the context, produce an output, check that output against a defined goal, fix what’s weak, run the cycle again. It keeps running until the output clears the criteria you set. You defined the goal and the standard. The agent runs the loop.
The analogy: a writer revising their own manuscript. They research, draft, read it back with fresh eyes, mark the weak sections, fix them, and read again. They don’t come back to the editor between every revision asking what to do next. The cycle is internal. It runs until the piece is good enough.
That’s a single agent loop. You’ve handed over the revision cycle, not just the first draft
Two things make or break the system: what counts as passing the evaluation, and when the loop stops. That’s the engineering work. Almost none of it requires a better model. Now, one agent is not enough, let me explain why and how we get to multiple loops…
