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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
Boris and Cat, the team behind Claude Code, explain how a terminal tool grew out of an internal prototype after an engineer was told to use it instead of hand-writing code. They cover why giving the model bash and a few tools worked better than building a custom toolkit.
- Claude Code began as an internal research tool called Clyde. A new engineer's hand-written PR got rejected with "use Clyde," and the one-shot result convinced the team that an IDE might not be necessary.
- The turning point was giving the model bash. It started writing AppleScript and automating things on its own, which the team had not expected.
- Anthropic "dogfoods" the product: roughly 70 to 80 percent of technical staff use it daily, and internal feedback arrives constantly, so features get shipped or pulled within days.
- The team has been removing tools over time. The LS tool was dropped once bash permissions could enforce directory restrictions, which leaves less clutter in the model's context.