Knowing When to Stop: The Art of Making a Loop Converge
This article explores the challenge of defining 'done' in AI-generated work, noting that humans rely on external signals like deadlines and peer review. It suggests that as we move toward autonomous loop engineering, the quality of the system depends entirely on the effectiveness of the automated verifiers.
Why it matters
As AI agents become more autonomous, the ability to programmatically define success criteria is becoming the most critical bottleneck in software development.
How can an AI model know when its work is done?
Well, how does a human know when our work is done.
A programmer waits for the tests to turn green or waits for PR review from their team. A designer adjusts a composition, steps away, returns, and decides the remaining imperfections no longer matter. A writer submits a draft because the deadline has arrived or because an editor accepts it, not because the prose has reached some objectively final state.
"Done" is rarely a property of the work itself. It is a judgment produced by the system around the work. Humans do not possess a universal detector for "done". We rely on a patchwork of signals like tests, specifications, precedent, approval, deadlines, risk, and finding that point of diminishing returns. In each case, completion comes from outside the work itself.
An AI model can almost always produce another answer.
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