Entropy Quest of Agentic AI: Towards a Negentropic Framework

Abstract

Abstract

The rapid proliferation of Agentic AI—autonomous systems capable of goal-directed reasoning and stepwise task deployment—is increasingly framed as a solution to organizational complexity. However, as these systems integrate into high-stakes environments, they face a fundamental challenge in the accumulation of operational and informational uncertainty in the organization. In the physical and information sciences, this disorganization is defined as entropy, originated as a term in thermodynamics and now encompasses the natural tendency of systems to transition from ordered to disordered states (Müller, 2007). While traditional IS acts as a negentropic force by organizing data, the introduction of autonomous agents introduces new layers of "emergent entropy" through unpredictable agent-environment interactions. Current literature in management and systems science provides a foundation for understanding entropy as a measure of systemic chaos. Organizational entropy manifests as bureaucratic friction and communication silos that degrade strategic alignment (Beer, 1981). In digital contexts, entropy is viewed to quantify the uncertainty inherent in information flows, where higher entropy necessitates greater computational work to restore predictability(Sun et al. 2024) (Murphy et al. 2024). Despite these foundations, there remains a notable research gap regarding the intersection of entropy and Agentic AI. We propose an interdisciplinary framework to investigate AI agents as active negentropic managers. This perspective views machine learning not just as pattern recognition but as an entropy-reduction process in which certain functions align model predictions with ordered reality (Goodfellow, Bengio et al., 2016). In enterprise architectures, Agentic AI functions by ingesting high-entropy, unstructured inputs and converting them into low-entropy, deterministic actions that improve decision-making reliability (Russell and Norvig, 2020). Hence, this research seeks to address: (1) How can entropy be measured in today’s AI-infused business environments, such as within ML-integrated workflows? and (2) How do agentic autonomous behaviors impact organizational and procedural reliability in enterprises? Theoretically, this study launches a formal, quantitative method to theorize and measure digital complexity and systemic disorder, moving beyond qualitative assessments toward formal, testable propositions (Baskerville & Myers, 2002). Practically, an entropy-calibrated framework equips practitioners with quantitative indicators—such as entropy and complexity—to assess technology defects and system fragility. Ultimately, this research aims to transition entropy from an analytical lens into an actionable empirical measurement framework (Dafoe, 2018), enabling the architecture of resilient autonomous infrastructures and robust AI entropy auditing capabilities.