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：本文探讨了基于多智能体系统理论（SMA）并结合大型语言模型（LLM）的计算实体所具备的动态影响力。这类实体能够模拟复杂的人类交互，通过运用专业人工智能体支持从组织运营流程到基于应用知识和人类编排的战略决策，为革新人类用户交互提供了可能性。先前研究表明，人工智能体在自主处理新挑战与实用任务（如引发逻辑推理和问题解决）时存在局限性。同时，传统技术（如思维链激发）需要明确的人工引导。在我们的方法中，采用了基于大型语言模型（LLM）开发的智能体，每个智能体通过考虑行为要素的不同原型设计，并由基于场景用例（角色扮演）促进知识生成的策略驱动，采用智能体间的讨论式交互（引导式对话）。我们展示了基于多智能体系统理论（SMA）和大型语言模型创新应用（LLM based）开发适用于组织战略的智能体的潜力，为不同应用、复杂度、领域和LLM能力提供了差异化且可适配的实验方案。