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Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions

Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani, Sajjadur Rahman, Tom Mitchell, Estevam Hruschka

TL;DR

This work tackles the challenge of deploying enterprise-grade multi-agent systems that orchestrate LLMs under real-world constraints. It introduces reasoning capacity (RC), an information-theoretic metric, to quantify how well a MAS reasons under constraints and to diagnose bottlenecks by decomposing the system into planners, orchestration, and agents. Through a HR-enterprise example, the authors illustrate RC computation, component-wise evaluation, and the role of self-reflection with human feedback in mitigating planning, data, and model limitations. The proposed framework aims to enable budget-aware optimization, robust operation in dynamic environments, and principled, human-centered improvements for reliable, privacy-conscious deployment of multi-agent AI systems.

Abstract

Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards practical adoption of LLMs, multi-agent systems hold great promise to augment, integrate, and orchestrate LLMs in the larger context of enterprise platforms that use existing proprietary data and models to tackle complex real-world tasks. Despite the tremendous success of these systems, current approaches rely on narrow, single-focus objectives for optimization and evaluation, often overlooking potential constraints in real-world scenarios, including restricted budgets, resources and time. Furthermore, interpreting, analyzing, and debugging these systems requires different components to be evaluated in relation to one another. This demand is currently not feasible with existing methodologies. In this postion paper, we introduce the concept of reasoning capacity as a unifying criterion to enable integration of constraints during optimization and establish connections among different components within the system, which also enable a more holistic and comprehensive approach to evaluation. We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system. We then argue how these limitations can be addressed with a self-reflective process wherein human-feedback is used to alleviate shortcomings in reasoning and enhance overall consistency of the system.

Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions

TL;DR

This work tackles the challenge of deploying enterprise-grade multi-agent systems that orchestrate LLMs under real-world constraints. It introduces reasoning capacity (RC), an information-theoretic metric, to quantify how well a MAS reasons under constraints and to diagnose bottlenecks by decomposing the system into planners, orchestration, and agents. Through a HR-enterprise example, the authors illustrate RC computation, component-wise evaluation, and the role of self-reflection with human feedback in mitigating planning, data, and model limitations. The proposed framework aims to enable budget-aware optimization, robust operation in dynamic environments, and principled, human-centered improvements for reliable, privacy-conscious deployment of multi-agent AI systems.

Abstract

Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards practical adoption of LLMs, multi-agent systems hold great promise to augment, integrate, and orchestrate LLMs in the larger context of enterprise platforms that use existing proprietary data and models to tackle complex real-world tasks. Despite the tremendous success of these systems, current approaches rely on narrow, single-focus objectives for optimization and evaluation, often overlooking potential constraints in real-world scenarios, including restricted budgets, resources and time. Furthermore, interpreting, analyzing, and debugging these systems requires different components to be evaluated in relation to one another. This demand is currently not feasible with existing methodologies. In this postion paper, we introduce the concept of reasoning capacity as a unifying criterion to enable integration of constraints during optimization and establish connections among different components within the system, which also enable a more holistic and comprehensive approach to evaluation. We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system. We then argue how these limitations can be addressed with a self-reflective process wherein human-feedback is used to alleviate shortcomings in reasoning and enhance overall consistency of the system.
Paper Structure (14 sections, 4 equations, 1 figure)

This paper contains 14 sections, 4 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Multi-Agent Systems
  3. Multi-Agent System in an HR Enterprise
  4. Reasoning Capacity
  5. Leveraging Reasoning Capacity to Connect Components of a Multi-Agent System
  6. Reasoning Capacity in the HR Company Example
  7. Bottlenecks in Orchestration Platform
  8. Bottlenecks in Planning
  9. Bottlenecks in Intelligent Agents (LLMs)
  10. Self-Reflecting with Human Feedback
  11. Related Work
  12. Conclusion
  13. Broader Impact
  14. Planer Illustration

Figures (1)

  • Figure 1: Overview of multi-agent systems in an enterprise infrastructure: Cloud applications can utilize multi-agent system to cater for application needs. Multi-agent systems sits in between applications and enterprise infrastructure components such as a model repository, API services, and data lake. Multi-agent systems are comprised of (a) an orchestration platform, (b) planners, and (c) agents. Agent registry keeps record of available agents, while data registry is a repository for data in the enterprise data lake.