Six feedback loops guide AI coding agents from errors to systematic improvements

A team implementing a user account deletion feature discovered their AI coding agent missed critical issues like active subscriptions and shared object access. They developed six feedback loops across planning, coding, testing, review, release, and incident phases to capture these oversights. This process, called loop engineering, allows AI agents to observe implementation results and revise approaches systematically. The method requires providing agents with executable environments and specific failure contexts for proper inspection. Engineering leaders should focus on such feedback loop designs when creating AI-first development pipelines.