Assessing Agentic AI & AI Agents Concepts and Platforms Through the Lens of Traditional Multi-Agent Systems Principles

Abstract

Multi-Agent Systems are a long-standing subfield of artificial intelligence, with foundational concepts emerging as early as the 1980s. Multi-Agent Systems involve collections of autonomous agents interacting within an environment and organized within a system. Although early research showed strong potential, computational limitations at the time restricted real-world adoption. The recent rise of AI Agents and Agentic AI, especially driven by advances in Large Language Models, has renewed interest in Multi-Agent concepts. These developments have lead to the rise of a new wave of Agent-based platforms and applications. However, many of these modern implementations only partially reflect the theoretical foundations of traditional Multi-Agent Systems, often resulting in limited explicability, robustness, coordination, or scalability. As a first contribution in this paper, we clarify the evolving terminology surrounding 'AI Agents' and 'Agentic AI', through the lens of Traditional Multi-Agent Systems formalisms. We propose a synthesis combining theoretical rigor of Traditional Multi-Agent Systems (MAS) with current technological capabilities of Agentic AI systems, which is paving the way to what we call 'Modern MAS'. As a second contribution, we use the formalisms of Traditional MAS to compare contemporary Agentic AI platforms.