# Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach arXiv: https://arxiv.org/abs/2502.14000 ยท HTML: https://mlsanity.com/paper/2502.14000/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 9 of its 57 references that are in the map. - First posted on arXiv 19 Feb 2025. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 9 of 9) - [Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks](https://mlsanity.com/paper/2304.10031/) (2023) - [A Survey of Distributed Consensus Protocols for Blockchain Networks](https://mlsanity.com/paper/1904.04098/) (2019) - [A Framework for Neurosymbolic Robot Action Planning using Large Language Models](https://mlsanity.com/paper/2303.00438/) (2023) - [Comparing Software Developers with ChatGPT: An Empirical Investigation](https://mlsanity.com/paper/2305.11837/) (2023) - [How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering](https://mlsanity.com/paper/2312.11719/) (2023) - [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024) - [Human-machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-machine Systems](https://mlsanity.com/paper/2111.14681/) (2021) - [Technical Report: Model-Checking for Resource-Bounded ATL with Production and Consumption of Resources](https://mlsanity.com/paper/1504.06766/) (2015) - [Reasoning about Strategies: on the Satisfiability Problem](https://mlsanity.com/paper/1611.08541/) (2016) ## Built on by (most cited first, 22 of 22) - [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025) - [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025) - [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025) - [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025) - [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025) - [A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents](https://mlsanity.com/paper/2506.23844/) (2025) - [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025) - [BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web](https://mlsanity.com/paper/2508.13787/) (2025) - [PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping](https://mlsanity.com/paper/2504.19818/) (2025) - [The Behavioral Fabric of LLM-Powered GUI Agents: Human Values and Interaction Outcomes](https://mlsanity.com/paper/2601.16356/) (2026) - [Quantum Abduction: A New Paradigm for Reasoning under Uncertainty](https://mlsanity.com/paper/2509.16958/) (2025) - [Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination](https://mlsanity.com/paper/2607.14641/) (2026) - [Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks](https://mlsanity.com/paper/2504.12446/) (2025) - [Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds](https://mlsanity.com/paper/2506.09335/) (2025) - [Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments](https://mlsanity.com/paper/2509.16676/) (2025) - [Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces](https://mlsanity.com/paper/2510.19008/) (2025) - [AI as intermediary in modern-day ritual: An immersive, interactive production of the roller disco musical Xanadu at UCLA](https://mlsanity.com/paper/2511.06195/) (2025) - [Hardware-Enforced Semantic Coordination for Safety-Critical Real-Time Autonomous Systems](https://mlsanity.com/paper/2607.02376/) (2026) - [The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce](https://mlsanity.com/paper/2607.13998/) (2026) - [From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar](https://mlsanity.com/paper/2607.18961/) (2026) - [Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety](https://mlsanity.com/paper/2608.14306/) (2026) - [What Counts as Strategic Reasoning? A Systematic Mapping of Chess Research on Humans, Engines, and Language Models](https://mlsanity.com/paper/2609.18286/) (2026) ## Abstract (arXiv) 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. A key distinction is made between multi-agent systems (MAS) and Centaurian systems, which represent two different paradigms of human-AI collaboration. MAS maintain agent autonomy, with structured protocols enabling cooperation, while Centaurian systems deeply integrate human and AI capabilities, creating unified decision-making entities. 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. 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.