标题： 代理人工智能与多代理人工智能：我们在 reinvent 轮子吗？ 显示英文标题

标题： Agentic AI and Multiagentic: Are We Reinventing the Wheel?

摘要： 自主人工智能（Agentic AI）和多自主人工智能（Multiagentic AI）这两个术语最近在关于生成式人工智能的讨论中变得流行起来，通常用来描述自主软件代理及其组成的系统。 然而，这些术语的使用混淆了这些流行语与人工智能文献中的成熟概念：智能代理和多智能体系统。 本文对这种概念误用进行了批判性分析。 我们回顾了“自主”的社会科学研究起源（Bandura，1986年）以及意图论哲学概念（Dennett，1971年），然后总结了Wooldridge、Jennings等人关于智能代理和多智能体系统的奠基性工作。 我们审视了经典的代理架构，从简单的反应代理到信念-欲望-意图（BDI）模型，并强调定义人工智能代理性的关键属性（自治性、反应性、主动性、社交能力）。 然后我们讨论了基于大型语言模型（LLMs）及其代理平台的最新进展，包括LLM驱动的AI代理和开源多代理编排框架的出现。 我们认为，AI自主通常被用作AI代理的流行语，而AI多自主则被用于多智能体系统。 这种混淆忽视了数十年来在自主代理和多智能体系统领域的研究。 文章提倡科学和技术严谨性，并采用人工智能领域的最新术语，将现有的丰富知识融入基于LLM的AI代理的新一波浪潮中，包括多智能体系统平台的标准、通信语言以及协调与合作算法，避免重蹈覆辙。 显示英文摘要

摘要： 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.