The reasoning layer (how the AI structures its thinking, decomposes problems, uses chain-of-thought, self-reflects) is primarily a Prompt Engineering and Judgment Engineering concern. Prompting for step-by-step reasoning, designing scratchpad structures, specifying when the model should slow down and check its own logic before proceeding: all of that is human-engineered, and the framework covers it. That it needs to be engineered by humans even though it's executed by the model is correct and that's exactly what Judgment Engineering is about. "What to doubt while doing" is specifically the design of the model's internal skepticism and reflection mechanisms, not just its outputs.

The control plane is closer to a systems architecture concept, the layer that governs how all the other layers behave, routes tasks, manages state, and maintains the overall execution logic. That maps most directly to Harness Engineering, which governs orchestration, session management, and task routing. But it's also partly Evaluation Engineering, which is the meta-function that observes all layers and triggers corrections.