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