Agentic Burnout and Epistemic Fatigue at Machine Speed: How High-Velocity AI Output Accelerates Managerial Cognitive Exhaustion

Abstract

The rapid deployment of autonomous agentic systems and large language models within modern organizational structures has fundamentally reconfigured managerial labor. Where traditional information management paradigms focused on resolving data scarcity through automated retrieval, contemporary agentic architectures produce unprecedented volumes of high-velocity, pre-optimized synthetic knowledge. This shift transforms managerial work from operational execution to continuous, high-speed critical validation. This conceptual study examines the emergent phenomenon of agentic burnout and epistemic fatigue, theoretical constructs describing the cognitive exhaustion resulting from perpetual critical verification under machine-driven velocity pressures. Integrating Cognitive Load Theory with the Job Demands-Resources (JD-R) model, this paper establishes a novel theoretical framework explaining how hyper-rapid AI output functions as an unbuffered job demand that depletes cognitive and epistemic resources. We construct a formal comparative qualitative framework and a non-empirical structural model linking non-deterministic algorithmic velocity, decision-validation friction, and epistemic destabilization to systemic organizational exhaustion. Furthermore, the paper provides a comparative institutional analysis across corporate knowledge-intensive firms and public sector governance regimes, establishing translational governance principles and operational boundary conditions.