Loop Engineering: The Six Architectural Layers That Separate Self-Improving Agents From Expensive Function Calls
Most AI agents in production today are glorified function calls. They take an input, run inference, produce an output, and forget everything the moment the response streams back. Ship one on Monday, and it's exactly as smart on Friday six weeks later as it was on day one — same edge cases, same wrong answers, same manual overrides eating the ROI you promised leadership.
There's a term circulating in the agent-building community for the architectural fix to this: loop engineering. It's not a new model, a framework you pip install, or a prompt technique. It's a way of thinking about the difference between a system that answers and a system that compounds.
If you're running agents in production and they've stalled, this is almost certainly why.
The enterprise pain point, concretely
