Reality-Constrained Systems: A Framework for Reducing Drift in AI and Decision Systems

AI systems increasingly produce outputs that are coherent and useful while remaining subtly misaligned with reality. This paper introduces Reality-Constrained Systems, a framework for maintaining alignment between representations and real-world conditions under optimization pressure.The framework identifies a common structural failure mode across AI systems, organizations, and decision processes: measurable indicators become targets, proxies replace underlying reality, and systems drift while continuing to function. This condition is difficult to detect because outputs remain internally consistent.To address this, the paper outlines three core components:<br>• Reality Anchors (external grounding mechanisms)<br>• Cognitive Constraints (structured reasoning processes)<br>• Drift Diagnostics (misalignment detection systems)<br>The framework situates existing approaches such as retrieval-augmented generation, structured prompting, and evaluation methods as partial implementations of these components. It proposes a shift from improving outputs to maintaining continuous coupling between representation and reality.