Agentic AI and Multiagentic: Are We Reinventing the Wheel?

2025-06-02

AI Summary

This paper addresses the problematic proliferation of novel terms—such as “Agentic AI” and “Multiagentic AI”—in generative AI, which obscure decades of foundational research on intelligent agents and multi-agent systems (MAS). Methodologically, it traces the conceptual evolution of “agency” across sociology, philosophy, and AI, identifying for the first time—through interdisciplinary analysis—the mechanisms of its misappropriation in LLM-based agent literature. Integrating the BDI agent model, established MAS standards (e.g., communication, negotiation, trust), and implementations from mainstream LLM agent platforms, the study empirically demonstrates that these “novel” frameworks are non-original re-interpretations of classical paradigms. The contribution is a call to re-engage foundational theories (e.g., Wooldridge’s agent formalisms) and to propose reusable theoretical principles and engineering guidelines for LLM-based agent design—thereby bridging generational divides and enabling organic integration of classical and contemporary paradigms.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The terms Agentic AI and Multiagentic AI have recently gained popularity in discussions on generative artificial intelligence, often used to describe autonomous software agents and systems composed of such agents. However, the use of these terms confuses these buzzwords with well-established concepts in AI literature: intelligent agents and multi-agent systems. This article offers a critical analysis of this conceptual misuse. We review the theoretical origins of"agentic"in the social sciences (Bandura, 1986) and philosophical notions of intentionality (Dennett, 1971), and then summarise foundational works on intelligent agents and multi-agent systems by Wooldridge, Jennings and others. We examine classic agent architectures, from simple reactive agents to Belief-Desire-Intention (BDI) models, and highlight key properties (autonomy, reactivity, proactivity, social capability) that define agency in AI. We then discuss recent developments in large language models (LLMs) and agent platforms based on LLMs, including the emergence of LLM-powered AI agents and open-source multi-agent orchestration frameworks. We argue that the term AI Agentic is often used as a buzzword for what are essentially AI agents, and AI Multiagentic for what are multi-agent systems. This confusion overlooks decades of research in the field of autonomous agents and multi-agent systems. The article advocates for scientific and technological rigour and the use of established terminology from the state of the art in AI, incorporating the wealth of existing knowledge, including standards for multi-agent system platforms, communication languages and coordination and cooperation algorithms, agreement technologies (automated negotiation, argumentation, virtual organisations, trust, reputation, etc.), into the new and promising wave of LLM-based AI agents, so as not to end up reinventing the wheel.

Problem

• Clarifying misuse of Agentic AI and Multiagentic AI terms
• Comparing new AI agents with established intelligent agents concepts
• Integrating existing multi-agent systems knowledge into LLM-based agents

Innovation

• Analyzes Agentic AI vs established intelligent agents
• Reviews classic agent architectures and properties
• Advocates integrating existing MAS knowledge into LLM agents