The article argues that AI “loops”—self-prompting agents using a goal, context, evaluation, and an agent—outperform single-shot prompts for long-running tasks. It outlines key components, real examples like PR babysitters and bug fixers, and explains why better models, built-in loop commands, and maturing toolchains make loops practical now.

• Opus 4.6 completes half of 12-hour tasks, 6x better than last year's model, and Stripe restructured its whole codebase in a day
• PostHog used a loop to fix a 3-year-old query-engine bug and got an 11% speedup
• Loops work now because of more stable models, built-in loop commands, and mature harnesses/toolchains (subagents, MCP, context compaction)
• The engineer's role shifts from writing code to defining goals, evaluation criteria, and context rather than being replaced

This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.

• Boris Cherny landed 259 PRs in 30 days by having loops handle all the prompting instead of him
• Loops have evolved through five stages, from ReAct/AutoGPT to today's self-scheduling, crash-recoverable multi-agent systems
• The real difference from plain cronjobs is a built-in decision engine: loops read state, decide next steps, and validate results rather than just firing a fixed script
• Cherny's practical advice centers on self-verification, auto-permissions, and using /loop or /goal commands so agents catch their own mistakes