Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agent systems represent a strategic response to these challenges. Such systems strive for autonomously tackling user-prompted goals by decomposing them into manageable tasks and orchestrating their execution and result synthesis through a collective of specialized intelligent agents. Equipped with LLM-powered reasoning capabilities, these agents harness the cognitive synergy of collaborating with their peers, enhanced by leveraging contextual resources such as tools and datasets. While these architectures hold promising potential in amplifying AI capabilities, striking the right balance between different levels of autonomy and alignment remains the crucial challenge for their effective operation. This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment across various aspects inherent to architectural viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context interaction. It also includes a domain-ontology model specifying fundamental architectural concepts. Our taxonomy aims to empower researchers, engineers, and AI practitioners to systematically analyze the architectural dynamics and balancing strategies employed by these increasingly prevalent AI systems. The exploratory taxonomic classification of selected representative LLM-powered multi-agent systems illustrates its practical utility and reveals potential for future research and development.

本文提出了一个多维分类法，旨在分析自主的LLM驱动的多智能体系统在根据建筑观点的各个方面（如目标驱动的任务管理、智能体组成、多智能体协作和上下文交互）之间如何平衡自主性和对齐的动态相互作用。它还包括一个指定基本建筑概念的领域本体模型。我们的分类法旨在使研究人员、工程师和AI从业者能够系统地分析这些日益普遍的AI系统所采用的建筑动态和平衡策略。所选择的代表性LLM驱动的多智能体系统的探索性分类说明了它的实际效用，并揭示了未来研究和开发的潜力。

平衡自治和协调：基于多维度的多智能体架构自主型LLM动力的分类