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.

翻译：大型语言模型（LLM）彻底革新了人工智能领域，赋予其卓越的语言理解与生成能力。然而，面对需要深刻迭代思维过程的复杂互联任务时，LLM暴露出其固有局限性。自主LLM驱动多智能体系统正是应对这些挑战的战略性方案。此类系统通过将用户目标分解为可管理任务，并借助专业化智能体集群协调执行与结果合成，力求自主解决问题。这些智能体凭借LLM驱动的推理能力，通过工具与数据集等情境资源的增强，实现同伴协作的认知协同效应。尽管这类架构在增强AI能力方面具有巨大潜力，但在不同程度的自主性与对齐性之间寻求平衡，仍是其有效运行的核心挑战。本文提出一套全面的多维分类法，旨在分析自主LLM驱动多智能体系统如何在架构视角固有的目标驱动任务管理、智能体构成、多智能体协作及情境交互等维度中，动态协调自主性与对齐性的相互作用。该分类法同时包含定义基础架构概念的领域本体模型。我们的分类法旨在赋能研究人员、工程师及AI从业者，系统分析这些日益普遍的AI系统所采用的架构动态与平衡策略。通过对代表性LLM驱动多智能体系统的探索性分类，本文验证了该方法的实践效用，并揭示了未来研究与发展潜力。