警惕‘智能体’术语滥用，避免重复造轮子。
Agentic AI and Multiagentic: Are We Reinventing the Wheel?
- 区分'智能体'与'多智能体系统'的学术渊源，厘清概念边界。
- 指出当前LLM驱动的智能体本质仍是经典智能体系统。
- 呼吁用成熟框架提升新式智能体系统的科学性与协同能力。
近年来，‘智能体人工智能’和‘多智能体人工智能’在生成式AI讨论中广受关注，常被用来描述自主软件代理及其系统。然而，这些术语混淆了流行词汇与人工智能文献中已确立的概念：智能体与多智能体系统。本文对这一概念误用进行批判性分析。回顾了社会科学研究中‘代理性’（Bandura, 1986）及意向性哲学思想（Dennett, 1971）的理论起源，并总结了Wooldridge、Jennings等人的奠基性工作。梳理从简单反应式代理到信念-欲望-意图（BDI）模型的经典架构，强调自主性、反应性、主动性与社会能力等核心属性。接着讨论大语言模型（LLMs）及其代理平台的发展，包括基于LLM的智能体及开源多智能体编排框架的兴起。文章主张，‘人工智能智能体’常仅是‘人工智能代理’的噱头说法，‘多智能体’亦等同于‘多智能体系统’。这种混淆忽视了数十年来关于自主代理与多智能体系统的研究成果。论文倡导采用科学严谨态度，使用人工智能领域的现有术语与知识体系，融入多智能体系统平台标准、通信语言、协调合作算法、协议技术（如自动协商、论辩、虚拟组织、信任与声誉机制等），以避免在新兴的基于大语言模型的智能体浪潮中重复造轮子。
原文摘要 · Abstract (English)
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.
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