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

Paperclip is a platform that manages AI agents to streamline business operations. It allows users to set goals, hire agents, and monitor their performance from a centralized dashboard. Unlike traditional task managers, Paperclip integrates organizational structures and budget controls for efficient agent coordination.

• Paperclip orchestrates multiple AI agents as a unified company rather than just managing individual tasks, adding org charts, budget controls, and goal alignment on top.
• It runs locally via Node.js/pnpm with an embedded, self-managed PostgreSQL database, and can run multiple isolated companies from one deployment.
• Agents run on schedules or event triggers with built-in cost tracking to prevent budget overruns and maintain persistent state.
• The platform supports plugins, allowing extension beyond its core orchestration and governance features.