Peter Steinberger, Boris Cherny and Addy Osmani have shifted the focus from writing one-off prompts to designing loops that automate recurring steps in agentic coding tools. A loop bundles intent, context, action, evaluation, memory and a stop condition into a repeatable workflow. Instead of manually feeding the same repo context, deployment checklist or tests into each prompt, you build a script, skill or scheduled run that does it for you until a defined end point or when human judgment is needed. The real gain comes once the time you save per run, multiplied by the number of runs and the chance the loop stays working, outweighs the cost to build and maintain it. Steinberger offers a simple break-even formula: P × N × (S + R) > F, where F is setup cost, N is number of future tasks, S is minutes saved per task, R is avoided risk cost and P is the loop’s reliability. In one example, spending 90 minutes to write a “shipping skill” that saves 10 minutes per pull request plus 5 minutes of review drag pays off after about eight PRs. A four-hour daily repo-triage script paying back 35 minutes per day breaks even after ten workdays if it stays roughly 70% reliable. He frames this as a shift in your execution horizon: early on, your bottleneck is “hands” doing the work; later, it’s your own ability to spot what matters among many options. Treat loops like renewable trees in Minecraft—you still cut them down, but you plant saplings near your base so you stop wandering off for new resources. Good loops run with minimal babysitting. Better ones report evidence or build tests so each mistake makes the next run cheaper. That compounding effect turns a prompt into a growing system rather than a one-off request.