The core thesis is that we are currently building "Open Loop" systems and hoping they behave reliably.
It’s not just about unit tests, better prompting, or buying more tools. It is a fundamental architectural gap. We have excellent "Actuators" (LangChain, Vector DBs) that execute actions, but we lack the "Sensors" (continuous measurement) and the "Controller" (the operational logic to correct drift).
In Control Theory, an Open Loop system with stochastic components (y ~ P(y|x)) guarantees degradation over time. The post argues that you can't "tool" your way out of this—you need to build a governance layer that acts as the system's brain.
I propose mapping the AI stack to Control Theory components: 1. Actuators (Muscles): Tools like LangChain. They execute but are blind to meaning. 2. Constraints (Skeleton): JSON Schemas/Pydantic. They fix syntax but ignore semantics. 3. Sensors (Nerves): Golden Sets & Evals. The missing feedback loop in most stacks. 4. Controller (Brain): The Operating Model that closes the loop.
Happy to discuss the mapping of Control Theory to AI Engineering.