Agent Loop

An agent loop is a way of giving an AI a task not just once, but making it repeat a full cycle — run it, check where it went wrong, find the cause, fix it, and run it again — until it actually works. The AI turns that wheel on its own, without a person telling it "this is wrong" every single time.

Why this is needed shows up in one common scene. Ask a coding AI to build a program, and in 30 minutes you get something that runs and even passes the test the AI wrote for itself. But feed it real data, and it falls apart. The test was easy because the AI was grading its own homework.

So the loop always needs a partner: a scorecard the AI can't touch. Results get checked against real data, at real (uncut) scale, using a tool the AI didn't build. If the AI can also edit the scorecard, the loop stops being verification and becomes self-persuasion. It's like letting a chef taste their own cooking instead of having a customer judge it.

When an article says "what changed once we added the loop" — the same AI given the same task failed at first but succeeded later — it usually doesn't mean the model changed. It means this cycle was added.

1. Ask Claude or ChatGPT: "Write Python code that reads a household budget CSV and calculates monthly totals."
2. Don't just take the code and stop — follow up with: "Actually run this code yourself and show me the results. If there's an error, diagnose the cause, fix it, and run it again. Keep repeating until it passes."
3. That second instruction is the loop. The difference between "make it for me" and "run it, fix it, and run it again" is the difference between a toy and the real thing.
4. One step further — instead of a sample, upload an actual file and ask for the same thing. You'll see code that worked fine on the sample break down on blank cells or odd date formats in the real file. That's exactly the scene this method exists for. The trick is not letting the AI grade itself — adding the condition "check it against the file I gave you, not the test you wrote" makes it far more accurate.