Loop engineering is the discipline of turning repetitive AI work into a system that can act, evaluate its own results, remember failures, improve, stop and escalate — without a human prompting every step.
Shubham Saboo, Senior AI Product Manager at Google, lays out the nine components a reliable loop needs: goal, context, actions, tools, evaluation, memory, guardrails, escalation and stopping conditions. He then maps the framework onto workflows you can actually run — champion-versus-challenger prompt testing, customer-research saturation, devil's-advocate PRD review, browser-based UI/UX testing and follow-up automation.
Just as useful is the failure catalogue: loops break through drift, weak evals, runaway cost, latency and endless retries — and target, budget and stall conditions are the mechanisms that prevent it. A conceptual blueprint for product managers, AI engineers and founders building repeatable agent workflows.