From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence
Zihao Wang, Junming Zhang

TL;DR
This paper introduces BusiAgent, a multi-agent LLM framework that enhances enterprise decision-making by integrating advanced AI models with business strategies, demonstrating superior performance across various scenarios.
Contribution
The paper presents BusiAgent, a novel multi-agent framework combining CTMDP, entropy optimization, and Stackelberg games for improved business decision support.
Findings
Outperforms existing methods in solution quality.
Enhances coherence and client-focus in solutions.
Validates effectiveness across diverse business scenarios.
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,…
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