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For two years, getting useful work out of a coding agent meant being the loop yourself — prompt, read, prompt again. As models hold a hard problem for hours, the bottleneck moves: not 'can it write good code' but 'can it keep making progress on its own without losing the thread or declaring victory early.' Loop engineering is the discipline that answers that — you design the system that prompts the agent: discover work, attempt, get a feedback signal, self-correct, verify in a separate context, persist state on disk, decide what's next. This post lays out the architecture, the five building blocks, a worked worker/verifier loop in Python, the loops worth building first, the best practices, and an honest look at the risks (the 'confident token furnace'), with every flow rendered as a diagram.