Leveraging Large Language Models in Multiagent System

The combination of Large Language Models (LLMs) and Multi-Agent Systems (MAS) represents an artificial intelligence paradigm that empowers decentralized agents to reason, communicate, and coordinate with human-level flexibility. This review integrates the progress of LLM-based MAS, with focus on architectural innovation like neuro- symbolic architectures and decentralized coordination techniques, for improvement in healthcare, autonomous systems, and smart cities. 20 foundation studies analysis shows uses such as diagnostic error prevention (32%) and autonomous vehicle crash avoidance (37%). Challenges remain, however, such as computational expense (e.g., tenfold cloud costs for 100-agent systems), ethical hazards (e.g., 34% recruitment simulation bias), and latency problems (500–800ms decision-making delay). Scalability limitations also occur due to energy wastage in edge deployment. Priorities in the future are light-weight LLM models for real-time applications, global governance paradigms to tackle regulatory fragmentation, and inter-disciplinary collaboration to promote ethical accountability. With the appropriate balance between innovation and societal justice, LLM- based MAS can become effective instruments for scalable, human-centered problem-solving.