What Is Loop Engineering? Make AI Check Its Work Before It Delivers

Originally published on Baize AI Notes.

One-shot AI generation gives uneven results. The same task can come back accurate one time and far off the next. The usual cause is the process: nothing in it gives the model a chance to check its own output. Loop Engineering addresses that. It designs loops in which the AI verifies its result before handing it over.

An experienced engineer does not commit code straight after writing it. They run the tests, look for missed edge cases, and only then open a review. Loop Engineering builds that habit into how the AI works: plan, execute, observe the result, correct what is wrong, and repeat for a few rounds before delivering.

Figure: Plan | Execute | Observe | Correct. Each round is more accurate than the last; the result ships only when it meets the bar.

In practice there are usually three loops:

• A development loop built around evaluation metrics. Every change is checked against a defined standard of right and wrong.
• The agent’s own plan, execute, observe, correct loop. It handles complex multi-step tasks.
• A feedback loop in production. Problems found in real use go back into the system, so the next round performs better.

Once these loops run reliably, they leave behind reusable templates and evaluation sets. New tasks do not start from a blank design. For this reason Baize AI treats Skills and accumulated knowledge as direct outputs of Loop Engineering, with no separate workstream to produce them.

If your team already uses AI to generate code or content and quality swings from one run to the next, that is the point to design loops into the workflow.

Related: How Should a Small Team Design Its First AI Coding Workflow?, From Idea to Product: How to Design an End-to-End AI Delivery Path.

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