Open Access

ARTICLE

LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms

Harri Renney^1,*, Maxim Nethercott¹, Nathan Renney², Peter Hayes¹

1 Kaze Technologies, Kaze Consulting, Bath, UK
2 Computer Science Research Centre, University of the West of England, Bristol, UK

* Corresponding Author: Harri Renney. Email:

Journal on Artificial Intelligence 2026, 8, 231-257. https://doi.org/10.32604/jai.2026.078487

Received 31 December 2025; Accepted 04 March 2026; Issue published 17 April 2026

Abstract

This paper provides systemisation on the emerging design patterns and paradigms for Large Language Model (LLM)-enabled multi-agent systems (MAS), evaluating their practical utility across various domains, bridging academic research and industry practice. We define key architectural components, including agent orchestration, communication mechanisms, and control-flow strategies, and demonstrate how these enable rapid development of modular, domain-adaptive solutions. Three real-world case studies are tested in controlled, containerised pilots in telecommunications security, national heritage asset management, and utilities customer service automation. Initial empirical results show that, for these case studies, prototypes were delivered within two weeks and pilot-ready solutions within one month, suggesting reduced development overhead compared to conventional approaches and improved user accessibility. However, findings also reinforce limitations documented in the literature, including variability in LLM behaviour that leads to challenges in transitioning from prototype to production maturity. We conclude by outlining critical research directions for improving reliability, scalability, and governance in MAS architectures and the further work needed to mature MAS design patterns to mitigate the inherent challenges.

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Keywords

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