Get The Loop Engineering Field Guide

The honest decode of the sentence that put AI coding in a chokehold this week: Peter Steinberger (now at OpenAI) and Boris Cherny (creator and head of Claude Code) both said the same thing — stop prompting your agents; start designing loops that prompt them. Everyone repeated it; almost nobody could define it. This is the plain-English answer with the receipts the hype skips. Inside: what a loop actually is (a small program that prompts the agent, reads the output, decides if it is done, and re-prompts — you stop being the thing inside the loop and become its author); the real 5-year lineage (ReAct 2022, AutoGPT 2023, the Ralph loop in 2025, the /goal command in spring 2026, and the loops-supervising-loops orchestration that is genuinely new); the /loop vs /goal cheat sheet most people get wrong (/loop re-runs on a schedule; /goal runs until a checker model confirms your condition is true, then stops); the 6 building blocks (automations, worktrees, skills, connectors, subagents, memory) and the self-verification rule that keeps a loop honest (the maker is never the only grader); the cost truth nobody puts on the thumbnail (the loop, not the code, is now the expensive part — Uber capped engineers at $1,500 per tool per month after blowing its annual AI budget in four months) and the 3 hard stops that keep a loop from torching your budget (max iterations, no-progress detection, a dollar ceiling); the honest take on cheap models (one real lever — not the answer); and a closed-loop pre-flight checklist plus the prompt-engineer-vs-loop-engineer shift. The line to remember: build the loop, but build it like someone who intends to stay the engineer — not just the person who presses go.

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