Getting started with loops

2026-06-30 · Source: Claude Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, medium

Summary

The Claude Code team defines agentic loops as agents repeating work cycles until a stop condition is met, categorizing them by trigger, stop criteria, primitive, and task type. The article outlines four main loop types: turn-based, triggered by user prompts for short tasks; goal-based (/goal), initiated manually with verifiable exit criteria or turn caps (e.g., "stop after 5 tries"); time-based (/loop and /schedule), running on intervals for recurring work or external system interaction; and proactive loops, event or schedule-triggered for well-defined, long-running tasks like bug triage, often combining /schedule, /goal, skills, and dynamic workflows. It also provides guidance on maintaining code quality through clean codebases, self-verification skills, accessible documentation, and using secondary agents for code reviews. Additionally, strategies for managing token usage are discussed, including selecting appropriate models, defining clear stop criteria, piloting large runs, and utilizing scripts for deterministic tasks.

Key takeaway

For AI Engineers designing autonomous agents, understanding agentic loop types is crucial for efficient task execution and resource management. You should select loop primitives like /goal for tasks with verifiable exit criteria or /schedule for recurring work, ensuring clear stop conditions to manage token usage. Implement custom verification skills via SKILL.md and consider a second agent for code reviews to maintain output quality. This approach optimizes agent performance and reduces manual oversight.

Key insights

Agentic loops enable AI agents to autonomously repeat work cycles until specific stop conditions are met.

Principles

• Define clear stop conditions for agent loops.
• Encode verification steps as agent skills.
• Match loop type to task complexity.

Method

The article describes how to design agentic loops by categorizing them based on trigger, stop criteria, Claude Code primitive, and task appropriateness. It details turn-based, goal-based, time-based, and proactive loop implementations.

In practice

• Use SKILL.md to encode verification steps.
• Set /goal with explicit turn caps (e.g., "stop after 5 tries").
• Employ /schedule for cloud-based routines.

Topics

• Agentic Loops
• Claude Code
• AI Agents
• Code Quality
• Token Management
• Dynamic Workflows

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Claude Blog.