Cloud computing is revolutionizing how we build and run applications, but managing the complex workflows involved can be a real headache. Imagine describing what you want your application to do in plain English, and having an AI automatically generate the underlying cloud infrastructure for you. That's the promise of Action Engine, a new research project that uses large language models (LLMs) to automate the creation of Function as a Service (FaaS) workflows. FaaS is a serverless computing model that lets developers run individual functions without managing servers, offering incredible scalability and cost-efficiency. However, building these function workflows requires specialized knowledge and tedious manual coding. Action Engine aims to change that. By using LLMs, Action Engine interprets natural language queries from developers and translates them into executable workflow configurations. It smartly identifies the necessary functions from a repository and figures out how they should connect, ensuring smooth data flow between them. This not only simplifies the development process but also makes it much faster, allowing developers to quickly adapt to changing needs. The research shows Action Engine generates workflows with significantly higher accuracy than traditional LLM-only approaches, thanks to its novel data dependency management and platform-independent design. This means fewer errors, less debugging, and faster deployment times. While still in its early stages, Action Engine represents a major step towards democratizing cloud-native application development. It empowers developers of all skill levels to harness the power of the cloud without needing deep expertise in complex infrastructure management. The future of cloud computing may just be as simple as telling the AI what you want to achieve.