From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence

2025-08-21

AI Summary

In enterprise decision-making, large language models (LLMs) struggle to integrate fine-grained operational analytics with high-level strategic objectives, resulting in fragmented workflows and weak cross-level collaboration. To address this, we propose a multi-agent LLM framework for enterprise decision support. Our approach: (1) formulates dynamic agent modeling as an extended continuous-time Markov decision process; (2) introduces a generalized entropy metric to optimize multi-agent coordination efficiency; (3) designs a hierarchical Stackelberg game mechanism to align strategic intent with tactical execution; and (4) integrates an LLM-driven multi-agent system, context-aware Thompson sampling for prompt optimization, and end-to-end quality assurance. Experiments across diverse business scenarios demonstrate significant improvements in decision consistency, execution efficiency, and user satisfaction—validating the framework’s capability to generate coherent, customer-centric strategic decisions.

Technology Category

• Multiagent Systems: Multiagent Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have shown promising potential in business applications, particularly in enterprise decision support and strategic planning, yet current approaches often struggle to reconcile intricate operational analyses with overarching strategic goals across diverse market environments, leading to fragmented workflows and reduced collaboration across organizational levels. This paper introduces BusiAgent, a novel multi-agent framework leveraging LLMs for advanced decision-making in complex corporate environments. BusiAgent integrates three core innovations: an extended Continuous Time Markov Decision Process (CTMDP) for dynamic agent modeling, a generalized entropy measure to optimize collaborative efficiency, and a multi-level Stackelberg game to handle hierarchical decision processes. Additionally, contextual Thompson sampling is employed for prompt optimization, supported by a comprehensive quality assurance system to mitigate errors. Extensive empirical evaluations across diverse business scenarios validate BusiAgent's efficacy, demonstrating its capacity to generate coherent, client-focused solutions that smoothly integrate granular insights with high-level strategy, significantly outperforming established approaches in both solution quality and user satisfaction. By fusing cutting-edge AI technologies with deep business insights, BusiAgent marks a substantial step forward in AI-driven enterprise decision-making, empowering organizations to navigate complex business landscapes more effectively.

Problem

• Reconcile operational analyses with strategic goals across markets
• Address fragmented workflows and reduced cross-level collaboration
• Handle hierarchical decision processes in complex corporate environments

Innovation

• Extended CTMDP for dynamic agent modeling
• Generalized entropy measure optimizes collaborative efficiency
• Multi-level Stackelberg game handles hierarchical decisions