LLM-Powered Conversational Multi-Agent Cognitive System for Collaborative Task Solving - Silva, Eryck; Santos, Frances A.; Thompson, Pedro; Reis, Julio C. dos (Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC); 2025: Anais do XIX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações, 2025)
Multi-agent system architectures powered by Large Language Models (LLMs) present a promising solution for tackling complex problems through autonomous collaboration. However, key challenges persist, including the need for seamless communication and coordination among agents, computational efficiency, behavioral consistency, and effective memory management. This study proposes a novel multi-agent system architecture in which an orchestrator agent dynamically distributes tasks among specialized LLM-powered agents. Our approach enables efficient task execution and intelligent decision support. We present a case application via an early implementation of our solution to identify factors influencing system effectiveness in terms of the agents’ memory results.