How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex

Automated agent loops represent a fundamental shift from manual, message-turn-based prompting to autonomous, goal-oriented workflows. By utilizing scheduled triggers—such as heartbeats, crons, or webhooks—agents independently execute repetitive tasks like triaging GitHub issues, managing calendars, or validating code quality. Implementing these loops involves defining clear success criteria and leveraging sub-agents to handle specific sub-tasks, ensuring work remains isolated and consistent. While these systems significantly increase productivity by allowing agents to prompt themselves until a specific outcome is achieved, they require careful design to manage token consumption and prevent inefficient execution. Effective loop engineering demands precise instructions and robust validation mechanisms to ensure that autonomous agents deliver high-quality results without constant human intervention.