Abstract

Artificial intelligence systems frequently exhibit behavioral drift under extended operation, partial information, or conflicting constraints. This work demonstrates that such drift can be systematically calibrated by explicitly declaring system operating regimes rather than relying on implicit heuristics or post‑hoc constraint enforcement. By formalizing assumptions related to coherence, symmetry, and correction pathways, drift becomes a bounded and analyzable dynamic rather than an uncontrolled failure mode. The approach is architecture‑agnostic and compatible with existing AI systems, requiring no modification to underlying models. Declared operating regimes improve interpretability, reproducibility, and resilience while preserving adaptive capacity. This paper presents a minimal structural framework for drift calibration and outlines validation checks that transform common failure concerns into explicit, testable configuration domains.