The concept of an AI 'loop' is an evolution, not a revolution. It applies traditional, time-tested automation triggers—such as scheduled cron jobs or event-driven webhooks—to initiate and control modern AI agents, providing a familiar foundation for developers.
Instead of focusing on complex technical workflows, design loops by outlining a specific job to be done for an agent, just as you would when onboarding a new human employee. This managerial mental model simplifies the design process and makes it more accessible.
Goal-based loops run until an outcome is validated. If the success criteria are poorly defined, the agent will continuously burn tokens in a potentially fruitless effort. This makes precise prompt engineering and evaluation criteria critical for cost control.
The most sophisticated loops don't execute all work in a single thread. Instead, a primary agent identifies sub-tasks and instantiates new, specialized "sub-agents" to handle them autonomously. This creates a powerful, scalable hierarchy of automation.
To run autonomous agents effectively without them interfering with each other, foundational infrastructure is essential. This includes isolated work trees (sandboxes), reusable skills (functions), and clear state tracking to ensure consistency and avoid conflicts.