Epistemic Autonomy through Deep Neural Cognition and the Evolution of Agentic Intelligence

Abstract

The concept of epistemic autonomy in artificial intelligence has emerged as a critical frontier in the development of autonomous systems capable of self-directed knowledge acquisition and adaptive reasoning. Traditional AI architectures, while highly effective in task-specific domains, lack the capacity to form, validate, and revise their own internal representations in response to novel and dynamic environments. This paper explores the role of deep neural cognition in enabling epistemic autonomy, positioning deep learning not merely as a computational tool but as a framework for constructing agentic intelligence. By integrating principles from representational learning, meta-learning, and reinforcement-based adaptation, the study proposes a multi-layered architecture that supports autonomous knowledge construction, reasoning under uncertainty, and self-evolving decision-making. Furthermore, the research examines the implications of these architectures for the evolution of artificial agency, highlighting mechanisms for intentionality, internal model formation, and adaptive behavioral control. This investigation demonstrates how deep neural systems can transition from reactive computational entities to self-directed epistemic agents, providing both theoretical foundations and practical guidelines for the future development of intelligent autonomous systems. The findings suggest a roadmap toward artificial agents that exhibit robust, self-regulating cognition, bridging the gap between deep neural architectures and agentic autonomy in complex, real-world scenarios.