Looper is a tool designed to enhance the efficiency of AI coding by enforcing a strict workflow. It’s built as a bash wrapper around Codex, focusing on single-task iterations using a JSON backlog, which serves as both a task list and an audit trail. Unlike many AI coding tools that can be forgetful and require constant human input, Looper allows the AI to autonomously pull tasks from the backlog, complete them, and then conduct a review pass to either add more work or mark the project as done. This structured approach reduces the burden on the user and keeps the development process organized. The creator emphasizes the importance of observability in AI workflows, arguing that a structured system allows for easier tracking of changes and outcomes. Looper aims to strike a balance between speed and oversight. The system runs fast enough for the user to intervene if necessary, but not so fast that it becomes unmanageable. This is critical in preventing the user from becoming a bottleneck in the project. The review passes mimic the decision-making process of a senior developer, ensuring that the AI's output aligns with project requirements before finalizing any work. The article also touches on the potential for future enhancements, particularly model interleaving, which involves using different AI models for various tasks. This strategy leverages the strengths of multiple models to improve overall performance, especially on complex tasks. The upcoming features will allow users to utilize one model for task execution while another handles planning and review, optimizing both cost and quality. Overall, Looper represents a shift toward more reliable and structured AI coding practices, moving away from the chaotic nature of free-form AI interactions.