  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.
