Human-artificial interaction in the age of agentic AI: a system-theoretical approach

This paper presents a novel perspective on human-computer interaction (HCI), framing it as a dynamic interplay between human and computational agents within a networked system. Going beyond traditional interface-based approaches, we emphasize the importance of coordination and communication among heterogeneous agents with different capabilities, roles, and goals. The paper distinguishes between Multi-Agent Systems (MAS)—where agents maintain autonomy through structured cooperation—and Centaurian systems, which integrate human and AI capabilities for unified decision making. To formalize these interactions, we introduce a framework for communication spaces, structured into surface, observation, and computation layers, ensuring seamless integration between MAS and Centaurian architectures, where colored Petri nets effectively represent structured Centaurian systems and high-level reconfigurable networks address the dynamic nature of MAS. We recognize that elements such as task recommendation, feedback loops, and natural language interfaces are common in contemporary adaptive HCI. What distinguishes our framework is not the introduction of these elements per se, but the synthesis of architectural principles that systematically accommodate both autonomy-preserving and integration-seeking configurations within a shared formal foundation. Our research has practical applications in autonomous robotics, human-in-the-loop decision making, and AI-driven cognitive architectures, and provides a foundation for next-generation hybrid intelligence systems that balance structured coordination with emergent behavior.