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.

本文探讨了基于多代理系统理论（SMA）与大型语言模型（LLM）的计算实体的动态影响，这些实体的特点是能够模拟复杂的人类互动，作为一种从使用专门的人工代理支持运营业务流程到基于应用知识和人类编排的战略决策的革命可能性。我们展示了利用基于大型语言模型（LLM）的代理开发，通过考虑行为元素并由策略驱动，从而刺激基于使用案例在商业场景中生成知识的能力，利用代理之间的讨论方法（引导式对话），为组织战略提供潜力，基于多代理系统理论（SMA）和创新的大型语言模型（LLM）的应用，为不同的应用、复杂性、领域和 LLM 的能力提供了一种不同和可适应的实验。