Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling service-oriented architectures. Yet, in the rapid evolving landscape of programming frameworks and new protocols, deploying and testing AI agents as distributed systems remains a daunting and labor-intensive task. We present DMAS-Forge, a framework designed to close this gap. DMAS-Forge decouples application logic from specific deployment choices, and aims at transparently generating the necessary glue code and configurations to spawn distributed multi-agent applications across diverse deployment scenarios with minimal manual effort. We present our vision, design principles, and a prototype of DMAS-Forge. Finally, we discuss the opportunities and future work for our approach.

翻译：智能体AI应用日益依赖具有不同角色、专用工具并能访问记忆层的多个智能体来解决复杂任务——这与面向服务的架构高度相似。然而，在快速演变的编程框架和新协议环境中，将AI智能体作为分布式系统进行部署和测试仍然是一项艰巨且劳动密集型的任务。我们提出了DMAS-Forge，一个旨在弥合这一差距的框架。DMAS-Forge将应用逻辑与具体的部署选择解耦，旨在透明地生成必要的粘合代码与配置，从而以最少的人工工作量，在各种部署场景中启动分布式多智能体应用。我们阐述了我们的愿景、设计原则以及DMAS-Forge的原型。最后，我们讨论了该方法带来的机遇与未来工作方向。