TY  - EJOU
AU  - Renney, Harri 
AU  - Nethercott, Maxim 
AU  - Renney, Nathan 
AU  - Hayes, Peter 

TI  - LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns \& Paradigms
T2  - Journal on Artificial Intelligence

PY  - 2026
VL  - 8
IS  - 1
SN  - 2579-003X

AB  - 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.
KW  - Multi-agent systems (MAS); agent coordination; human-agent interaction; human-in-the-loop; large language models (LLMs); automation; Single Information Environment (SIE)

DO  - 10.32604/jai.2026.078487