Function as a Service (FaaS) is poised to become the foundation of the next generation of cloud systems due to its inherent advantages in scalability, cost-efficiency, and ease of use. However, challenges such as the need for specialized knowledge and difficulties in building function workflows persist for cloud-native application developers. To overcome these challenges and mitigate the burden of developing FaaS-based applications, in this paper, we propose a mechanism called Action Engine, that makes use of Tool-Augmented Large Language Models (LLMs) at its kernel to interpret human language queries and automates FaaS workflow generation, thereby, reducing the need for specialized expertise and manual design. Action Engine includes modules to identify relevant functions from the FaaS repository and seamlessly manage the data dependency between them, ensuring that the developer's query is processed and resolved. Beyond that, Action Engine can execute the generated workflow by feeding the user-provided parameters. Our evaluations show that Action Engine can generate workflows with up to 20\% higher correctness without developer involvement. We notice that Action Engine can unlock FaaS workflow generation for non-cloud-savvy developers and expedite the development cycles of cloud-native applications.

翻译：函数即服务（FaaS）因其在可扩展性、成本效益和易用性方面的固有优势，有望成为下一代云系统的基础。然而，对于云原生应用开发者而言，仍存在诸如需要专业知识以及构建函数工作流困难等挑战。为克服这些挑战并减轻开发基于FaaS应用的负担，本文提出一种名为Action Engine的机制，其核心利用工具增强的大语言模型（LLM）来解析人类语言查询，并实现FaaS工作流的自动生成，从而减少对专业知识和手动设计的依赖。Action Engine包含从FaaS仓库中识别相关功能模块以及无缝管理其间数据依赖关系的组件，确保开发者的查询得到处理与解决。此外，Action Engine能够通过注入用户提供的参数来执行生成的工作流。我们的评估表明，在无需开发者介入的情况下，Action Engine生成的工作流正确率最高可提升20%。我们注意到，Action Engine能够为非云技术背景的开发者开启FaaS工作流生成能力，并加速云原生应用的开发周期。