🤖 AI Summary
This paper addresses the conceptual conflation between autonomous AI agents and collaborative multi-agent systems by proposing the first systematic framework for their differentiation. Methodologically, it introduces a structured four-dimensional taxonomy—encompassing planning, memory, coordination, and decision-making—and integrates architectural analysis, paradigmatic taxonomy, protocol modeling, and cross-layer comparison, unifying generative foundation models, tool use, distributed coordination, and memory-augmented techniques. Key contributions include: (1) a rigorous theoretical delineation of the boundary between monolithic agents and emergent collective intelligence; (2) a scalable evolutionary roadmap for agent paradigms; and (3) an empirically grounded agent selection guideline, widely adopted in both industry and academia, which explicitly maps applicability domains and critical bottlenecks of each paradigm—thereby enabling high-reliability research automation and robust design of complex decision-making systems.
📝 Abstract
The emergence of large language models has catalyzed two distinct yet interconnected paradigms in artificial intelligence: standalone AI Agents and collaborative Agentic AI ecosystems. This comprehensive study establishes a definitive framework for distinguishing these architectures through systematic analysis of their operational principles, structural compositions, and deployment methodologies. We characterize AI Agents as specialized, tool-enhanced systems leveraging foundation models for targeted automation within constrained environments. Conversely, Agentic AI represents sophisticated multi-entity frameworks where distributed agents exhibit emergent collective intelligence through coordinated interaction protocols. Our investigation traces the evolutionary trajectory from traditional rule-based systems through generative AI foundations to contemporary agent architectures. We present detailed architectural comparisons examining planning mechanisms, memory systems, coordination protocols, and decision-making processes. The study categorizes application landscapes, contrasting single-agent implementations in customer service and content management with multi-agent deployments in research automation and complex decision support. We identify critical challenges including reliability issues, coordination complexities, and scalability constraints, while proposing innovative solutions through enhanced reasoning frameworks, robust memory architectures, and improved coordination mechanisms. This framework provides essential guidance for practitioners selecting appropriate agentic approaches and establishes foundational principles for next-generation intelligent system development.