Why Adaptive AI Architectures Fail in Critical Systems

The increasing reliance on adaptive and continuously learning artificial intelligence systems in high-impact domains has exposed a critical mismatch between prevailing AI design paradigms and the foundational requirements of safety-critical engineering. While adaptive architectures excel in environments characterized by low risk tolerance, informal accountability, and rapid feedback cycles, their deployment in critical systems introduces structural instability, undermines auditability, and erodes controllability. This paper argues that adaptivity in production is not merely a technical choice, but an architectural liability in systems where failure carries irreversible legal, economic, or human consequences. Drawing upon established principles from critical systems engineering, this work demonstrates why adaptive AI architectures fail to provide the guarantees required for predictable behavior, formal verification, and accountable governance. Rather than proposing incremental safeguards, the paper establishes a clear engineering boundary: adaptive AI is fundamentally incompatible with the operational constraints of critical systems.