Agentic AI and Multiagentic: Are We Reinventing the Wheel?

TL;DR

This paper critically examines the misuse of the terms 'Agentic AI' and 'Multiagentic AI', emphasizing the importance of using established AI terminology and concepts to avoid reinventing foundational ideas in autonomous agents and multi-agent systems.

Contribution

It provides a comprehensive review of the origins and definitions of agency in AI, clarifies misconceptions, and advocates for rigorous terminology aligned with existing research and standards.

Findings

Clarifies the distinction between AI agents and multi-agent systems.

Highlights the importance of established architectures and standards in AI agent research.

Warns against the superficial use of 'agentic' terminology in LLM-based AI developments.

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.