Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems

2025-09-02

AI Summary

This paper addresses the theoretical tension and integration pathways between large language model (LLM)-driven agents and classical multi-agent systems (MAS). Methodologically, it establishes an integrative analytical framework combining systematic literature review, cross-paradigm model comparison, and structural synthesis, examining differences and synergies along three dimensions: autonomy, coordination mechanisms, and cognitive architecture. The study introduces the novel concept of “LLM-augmented MAS,” explicitly characterizing critical challenges—including interpretability, robust collaborative reasoning, and goal alignment—in current hybrid approaches. By bridging cutting-edge LLM-based agent practice with foundational distributed intelligence theory, the work derives principled design guidelines and evolutionary trajectories for trustworthy, scalable next-generation MAS. These contributions provide dual support for both theoretical modeling and practical engineering implementation.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

This contribution provides our comprehensive reflection on the contemporary agent technology, with a particular focus on the advancements driven by Large Language Models (LLM) vs classic Multi-Agent Systems (MAS). It delves into the models, approaches, and characteristics that define these new systems. The paper emphasizes the critical analysis of how the recent developments relate to the foundational MAS, as articulated in the core academic literature. Finally, it identifies key challenges and promising future directions in this rapidly evolving domain.

Problem

• Comparing LLM-driven agent advancements with classic Multi-Agent Systems
• Analyzing models and characteristics of new AI agent technologies
• Identifying challenges and future directions in agent technology evolution

Innovation

• LLM-driven agent advancements vs classic MAS
• Analysis of models, approaches, and system characteristics
• Identifies challenges and future research directions