@grapeot: Loop Engineering has become a buzzword lately, but what truly matters is not techniques like cron, worktree, or running multiple agents in parallel. These are useful but are merely implementation layers. The more fundamental shift is: we are encoding the second-order management operations of an AI Manager into the system…

The term Loop Engineering has become popular recently, but what truly matters isn’t the techniques like cron, worktree, or running several agents in parallel.

Those things are useful, but they are just implementation details.

The more fundamental shift is this: we are encoding the second-order management actions of an AI Manager into the system.

Previously, when using AI, the core actions were splitting tasks yourself, providing context, reviewing intermediate results, and deciding next steps. Modern coding agents can already write code, run verification, read error messages, and debug on their own. What really blocks people now is no longer “pasting the error back,” but that in long tasks you still have to act as a manager: decide the next step, diagnose failure modes, judge whether a change is truly an improvement, and repeatedly explain successful experiences to the next agent.

The Senior Manager approach is to systematize these actions:

• Use evaluation to give the system a fixed yardstick.
• Use observability and rationale to see why the model failed.
• Use SOPs / skills to turn a one-time successful coaching into a long-term capability.
• Use maker/checker to prevent the model from grading itself too leniently.
• Use real data feedback to create a data flywheel.

So the key to Loop Engineering is not making the agent run more rounds, but turning “how humans manage AI” into “how the system manages itself.”

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