The article argues that when you find yourself repeating the same prompts with AI agents, it’s smarter to build a “loop” – a system combining intent, context, action, evaluation, memory, and a stop condition – rather than manually steering each step. It shows how to calculate when a loop’s upfront cost pays off over repeated tasks and gives examples ranging from CI checks to goal-based agent scripts.

• Break-even formula for automating a task: P × N × (S + R) > F, where reliability, run count, and time/risk saved must outweigh setup cost.
• A 90-minute "shipping skill" script pays off after ~8 PRs if it saves 10 min per PR plus 5 min of review drag.
• A 4-hour repo-triage script saving 35 min/day breaks even in ~10 workdays at ~70% reliability.
• The real bottleneck shifts from doing the work by hand to noticing which recurring tasks are worth turning into a self-sustaining loop.