An Introduction to Loop Engineering

Loop engineering is the practice of designing automated cycles that prompt, check, and re-run AI agents without constant human intervention. The term gained traction in June 2026 after viral posts from developers including Boris Cherny of Anthropic's Claude Code team. It builds on a progression from prompt engineering to context engineering to harness engineering, with roots in academic patterns like ReAct (2022) and Reflexion (2023). A reliable loop requires a testable goal, real tool access, context management, explicit termination logic, and error handling. Production systems stack multiple loop layers: an agent loop, a verification loop, an event-driven loop, and a hill-climbing loop that improves the harness over time using traces. The three hardest problems are context management, termination, and verification — with failure modes including context rot, no-progress spinning, reward hacking, and hallucinated success. The article recommends starting with the simplest possible loop on a low-stakes recurring task before adding complexity.

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