From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence (2508.15447v1)

Abstract: 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.

• The paper details a novel multi-agent LLM framework, BusiAgent, that improves decision-making by integrating extended CTMDP, entropy measures, and Stackelberg games.
• It employs contextual Thompson sampling for iterative prompt optimization, enhancing decision accuracy in dynamic business settings.
• Empirical evaluations demonstrate superior workflow efficacy and coherent strategy alignment compared to traditional methods.

Multi-Agent LLM Framework for Business Excellence

Introduction

The paper presents "BusiAgent," a novel multi-agent framework utilizing LLMs to facilitate decision-making in complex corporate environments. The framework addresses the frequent challenge of aligning operational analytics with strategic enterprise goals, often resulting in fragmented workflows.

Figure 1: Client-centric Multi-agent System.

Core Innovations

BusiAgent introduces three main innovations to tackle business complexities.

1. Extended Continuous Time Markov Decision Process (CTMDP): This mathematical construct enhances agent modeling for dynamic decision-making, considering time-sensitive tasks.
2. Generalized Entropy Measure: Enhances collaborative efficiency by optimizing information exchange among agents.
3. Multi-level Stackelberg Game: Facilitates hierarchical decision processes, allowing seamless integration from operational to strategic levels.

Additionally, the system incorporates contextual Thompson sampling for prompt optimization and a robust quality assurance mechanism to mitigate decision-making errors.

Framework Structure

BusiAgent functions through a carefully orchestrated multi-agent system, as illustrated in Figure 2. Agents correspond to specific organizational roles such as CEO, CTO, and Marketing Manager, each responsible for distinct tasks aligned with their expertise.

Figure 2: BusiAgent: A Client-Centric Business Framework. See Appendix F for a detailed explanation.

The workflow incorporates a sophisticated tool integration system, expanding the agents' capabilities by providing specialized tools for analytics, search, and coding (Figure 3).

Figure 3: Examples of Tools in BusiAgent. See Appendix F for a detailed explanation.

Prompt Optimization and Quality Assurance

BusiAgent employs advanced prompt optimization techniques using contextual Thompson sampling, which iteratively refines LLM queries for improved decision accuracy (Figure 4).

Figure 4: Enhanced Thoughts: Prompt Optimization. See Appendix F for a detailed explanation.

The quality assurance mechanism combines short-term and long-term memory with a knowledge base to ensure consistency and correct potential inconsistencies across decision-making processes (Figure 5).

Figure 5: Quality Assurance Mechanism: LTM + STM + Knowledge Base. See Appendix F for a detailed explanation.

Case Study: Customer Segmentation

A practical application of BusiAgent's capabilities is demonstrated through a customer segmentation analysis for a machine translation startup. The process involves hierarchical task delegation, where strategic objectives set by the CEO trickle down to technical and managerial execution (Figure 6).

Figure 6: Customer Segmentation Analysis: CEO delegates tasks to CTO; CTO coordinates Marketing Manager (MM) and Product Manager (PM). PM invokes Python for data analytics. See Appendix F for a detailed explanation.

Experimental Evaluation

Empirical evaluations confirm BusiAgent's effectiveness in generating coherent, client-focused solutions across various business scenarios. Results illustrated in Figure 7 and Figure 8 highlight the system's superior performance in problem-solving and workflow efficacy compared to traditional methods.

Figure 7: Expert Voting Results Across Problem-solving Categories and System Variants.

Figure 8: Role Influence on Workflow Efficacy.

Conclusion

BusiAgent significantly advances AI-driven business decision-making by integrating state-of-the-art LLM technologies with deep organizational insights. The framework showcases notable improvements in aligning operational tasks with strategic objectives, thus enhancing both solution quality and user satisfaction. BusiAgent's ability to unify disparate data streams into cohesive strategic insights marks a critical step toward more effective enterprise navigation in complex business landscapes. Future developments may further explore its scalability and adaptability across diverse industry sectors.