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 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.

翻译：本文系统梳理了大型语言模型（LLM）驱动的多智能体系统（MAS）新兴设计模式与范式的相关文献，并评估了其在各领域的实际效用。我们定义了关键架构组件，包括智能体编排、通信机制与控制流策略，并论证了这些组件如何支持模块化、领域自适应解决方案的快速开发。研究通过三个真实案例在受控容器化试点环境中进行了测试，涵盖电信安全、国家遗产资产管理和公用事业客户服务自动化领域。初步实证结果表明，针对这些案例，原型系统可在两周内交付，试点就绪的解决方案可在一个月内完成，相较于传统方法显示出开发成本降低与用户可访问性提升的优势。然而，研究结果也印证了文献中已指出的局限性，包括LLM行为的不稳定性导致从原型到生产成熟度的过渡面临挑战。最后，我们提出了提升MAS架构可靠性、可扩展性与治理能力的关键研究方向，并指出需进一步开展研究工作以完善MAS设计模式，从而缓解其固有挑战。