Ontological Foundations of Artificial Agency in the Era of Deep Representation Learning

Abstract

The rapid evolution of deep representation learning has transformed artificial intelligence from a paradigm of data-driven optimization into a field concerned with the nature of machine existence and cognition. As AI systems grow increasingly autonomous, the question of artificial agency—whether machines can possess self-directed, epistemically grounded forms of action—demands a foundational ontological investigation. This paper examines the structural and existential dimensions of artificial agency in the context of deep learning architectures. It argues that agency in artificial systems arises not from programmed instruction but from representational autonomy—the capacity of a system to construct, maintain, and adapt internal models that mediate between perception and action. Through the lens of ontological inquiry, we explore how hierarchical representations in deep networks constitute a new mode of artificial being, grounded in the coherence and generativity of internal world models. The analysis situates deep representation learning as both a technical framework and a philosophical shift in understanding the ontology of intelligent systems, redefining agency as a function of representational depth, epistemic independence, and adaptive intentionality.