The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware--software systems that integrate FMs as core components. While building demonstration-level FMware is relatively straightforward, transitioning to production-ready systems presents numerous challenges, including reliability, high implementation costs, scalability, and compliance with privacy regulations. This paper provides a thematic analysis of the key obstacles in productionizing FMware, synthesized from industry experience and diverse data sources, including hands-on involvement in the Open Platform for Enterprise AI (OPEA) and FMware lifecycle engineering. We identify critical issues in FM selection, data and model alignment, prompt engineering, agent orchestration, system testing, and deployment, alongside cross-cutting concerns such as memory management, observability, and feedback integration. We discuss needed technologies and strategies to address these challenges and offer guidance on how to enable the transition from demonstration systems to scalable, production-ready FMware solutions. Our findings underscore the importance of continued research and multi-industry collaboration to advance the development of production-ready FMware.

翻译：基础模型（FMs），例如大语言模型（LLMs）的快速扩展，催生了FMware——一种将FMs作为核心组件集成的软件系统。虽然构建演示级别的FMware相对简单，但过渡到生产就绪的系统则面临诸多挑战，包括可靠性、高昂的实施成本、可扩展性以及隐私法规的合规性。本文基于行业经验和多种数据源（包括亲身参与企业人工智能开放平台（OPEA）和FMware生命周期工程），对FMware生产化的关键障碍进行了主题分析。我们识别了FM选择、数据与模型对齐、提示工程、智能体编排、系统测试和部署等方面的关键问题，以及内存管理、可观测性和反馈集成等跨领域关切。我们讨论了应对这些挑战所需的技术和策略，并就如何实现从演示系统到可扩展、生产就绪的FMware解决方案的转型提供了指导。我们的研究结果强调了持续研究和跨行业合作对于推进生产就绪FMware开发的重要性。