Loop Engineering
Loop engineering is designing iterative cycles where an AI observes context, acts, checks results, and repeats until a task completes or a human stops it.
Agents, eval pipelines, and “keep improving until good enough” flows all depend on loops you design for visibility, limits, and exit conditions.
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What it means
A loop is plan → act → evaluate → revise. Engineering the loop means defining max iterations, success criteria, checkpoints, and what gets logged each pass.
Why designers should care
Bad loops feel infinite, opaque, or expensive. Good loops show progress, let users pause or steer, and surface when the model is stuck.
Example
A “Polish microcopy” feature runs up to three passes with a diff after each; users see what changed and click Stop after pass one if satisfied.
Common mistakes
- • Hidden retry loops that burn tokens and time without user awareness.
- • No stop condition, so agents churn until rate limits or timeouts.
- • Eval steps that never surface to users, so quality swings feel random.