LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms
Harri Renney, Maxim N Nethercott, Nathan Renney, Peter Hayes

TL;DR
This paper formalizes design patterns for LLM-enabled multi-agent systems, evaluating their practical utility through real-world case studies, highlighting rapid development benefits and ongoing challenges in reliability and scalability.
Contribution
It introduces a formal framework for LLM-MAS design patterns, demonstrating their application across domains and analyzing their advantages and limitations.
Findings
Prototypes developed within two weeks, pilot solutions within one month.
Reduced development overhead compared to traditional approaches.
Challenges remain in LLM behaviour variability affecting production readiness.
Abstract
This paper formalises the literature on emerging design patterns and paradigms for Large Language Model (LLM)-enabled multi-agent systems (MAS), evaluating their practical utility across various domains. 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…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Natural Language Processing Techniques · Artificial Intelligence in Law
