This article explores the dynamic influence of computational entities based on multi-agent systems theory (SMA) combined with large language models (LLM), which are characterized by their ability to simulate complex human interactions, as a possibility to revolutionize human user interaction from the use of specialized artificial agents to support everything from operational organizational processes to strategic decision making based on applied knowledge and human orchestration. Previous investigations reveal that there are limitations, particularly in the autonomous approach of artificial agents, especially when dealing with new challenges and pragmatic tasks such as inducing logical reasoning and problem solving. It is also considered that traditional techniques, such as the stimulation of chains of thoughts, require explicit human guidance. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements, driven by strategies that stimulate the generation of knowledge based on the use case proposed in the scenario (role-play) business, using a discussion approach between agents (guided conversation). We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based), offering a differentiated and adaptable experiment to different applications, complexities, domains, and capabilities from LLM.

翻译：本文探讨基于多智能体系统理论（SMA）与大型语言模型（LLM）相结合的计算实体之动态影响——这类实体以模拟复杂人类交互的能力为特征——作为通过运用专业知识与人工编排的专用智能体，从支持操作性组织流程到战略决策等一系列应用，彻底革新人机交互的可能性。既往研究表明，现有方法存在局限，尤其是在智能体的自主化方法中，当面对逻辑推理与问题解决等新型挑战与实用型任务时尤为突出。研究同时指出，思维链激发等传统技术需要明确的人类引导。在本文方法中，我们采用基于大型语言模型（LLM）开发的智能体，每个智能体具有考虑行为元素的差异化原型设计，并由基于商业场景用例（角色扮演）驱动知识生成的策略引导，采用智能体间的讨论方法（引导式对话）。我们展示了基于多智能体系统理论（SMA）与大型语言模型创新应用（基于LLM）开发适用于组织战略的智能体的潜力，提供了一种可差异化适配不同应用场景、复杂性、领域及LLM能力的实验方案。