The pursuit of Artificial General Intelligence faces systematic failures that current approaches cannot overcome: enterprise AI projects report 42–95% abandonment rates, frontier models exhibit 30–40% performance degradation in middle context positions, and multi-step agent tasks fail 41–87% of the time. We argue these failures share a common root cause: the absence of autonomous state management capability. We present Autonomous State Management (ASM)—a formal framework defining the capability to autonomously manage cognitive state through situational understanding rather than predefined rules. Drawing on neuroscience evidence (r = 0.5–0.77 correlation between working memory capacity and general intelligence) and cognitive architecture precedents, we argue ASM is a necessary condition for AGI. We contribute: (1) a formal definition distinguishing ASM from existing memory approaches, (2) seven observable behaviors mapping to executive function components, (3) four novel evaluation benchmarks, and (4) preliminary evidence from manual implementation.
A H (2026) studied this question.