Loop engineering builds upon prompt, context, and harness engineering to create autonomous, self-correcting AI agent loops.

Loop engineering represents an emerging methodology in agentic AI that shifts developer focus from manual turn-by-turn prompting to designing automated, self-correcting execution loops. Building on prompt engineering (defining instructions), context engineering (managing workspace memory), and harness engineering (providing execution infrastructure and tool interfaces), loop engineering governs the operational rhythm of AI tasks. It establishes explicit trigger predicates, termination conditions, failure recovery paths, and adversarial verification checks to enable reliable, multi-step autonomous problem solving without requiring constant human intervention.