Agentic loops represent a shift in AI-assisted development where instead of manually validating AI output, developers set up automated validation cycles. Using tools like Claude Code's /goal command, you can give an AI agent a high-level task with a checklist, and it will iterate autonomously — running tests, fixing failures, and checking off items — until the goal is complete. Practical examples include fixing failing GitHub Actions, improving Lighthouse scores, and implementing multi-phase features with built-in security, performance, and code quality audits. Multiple agents can run in parallel on different tasks like refactoring, security audits, and performance reviews, each with their own validation loops.
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