The paradigm of interacting with artificial intelligence is shifting from traditional prompt engineering to a new approach called "loop engineering." While prompt engineering focused on refining single commands to achieve desired outputs, loop engineering emphasizes iterative cycles of feedback and refinement. This evolution allows AI to autonomously correct errors and optimize results through continuous self-improvement loops. Industry experts suggest that this transition reflects a move toward more agentic AI systems capable of complex reasoning. By automating the iterative process, users can achieve higher precision without manually tweaking every prompt. This shift is expected to significantly enhance the efficiency of AI-driven workflows across various sectors. Consequently, the technical focus is moving from static input design to dynamic process management.