Loop Engineering

Simple Definition

Loop Engineering is the design of how an AI workflow repeats itself: when it checks its own work, when it retries after a failure, when it escalates to a human, and when it stops for good.

A basic loop looks like this: input, plan, act, verify, retry or escalate, done.

How It Differs From Simple Prompting

A single prompt asks a question and accepts whatever comes back. Loop Engineering assumes the first answer might be wrong, and builds a repeatable process around that assumption instead.

Simple prompting has no opinion about what happens if the output fails a check. Loop Engineering decides that in advance: retry with more context, try a different approach, ask a human, or stop and report the failure honestly.

What Loop Engineering Actually Decides

Retries: how many attempts before giving up, and what changes between attempts
Verification: what “done correctly” means, and how the loop checks for it
Stop rules: the conditions that end the loop, success, failure, or a budget running out
Escalation: when the loop hands the problem to a human instead of trying again
Budgets: limits on time, cost, or attempts so a loop can’t run forever
Success criteria: the concrete bar the output has to clear before the loop calls it finished

Example

A coding agent writes a fix, runs the test suite, and reads the result. If tests fail, it revises and retries, up to a set limit. If tests still fail after that limit, the loop stops and flags the issue for a person instead of retrying indefinitely or quietly shipping broken code.

Why It Matters

Without deliberate loop design, agents tend to fail in one of two ways: they stop too early and hand back weak work, or they retry forever and burn time and budget without making progress. Loop Engineering is what prevents both failure modes by making the retry and stop logic explicit instead of accidental.