Using (Traditional) Multi-Agent principles to suggest improvements for Agentic AI platforms
Résumé
Multi-Agent Systems (MAS) have been a key AI subfield since the 1980s, with strong academic foundations in intelligent agents and agent-oriented programming. The rise of LLMs in the 2020s has led to "AI Agents" with greater autonomy, renewing interest in MAS and inspiring new platforms. While traditional MAS researchers are starting to integrate LLMs, the emerging, industry-driven Agentic AI community often overlooks MAS formalisms, leading to narrower, less rigorous definitions. This paper uses MAS formalism to clarify Agentic AI concepts, highlight current limitations, and suggest paths for improvement.
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