In agile software development, user stories capture requirements from the user’s perspective, emphasizing their needs and each feature’s value. Writing concise and quality user stories is necessary for guiding software development. Alongside user story generation, prioritizing these requirements ensures that the most important features are developed first, maximizing project value. This study explores the use of Large Language Models (LLMs) to automate the process of user story generation, quality assessment, and prioritization. We implemented a multi-agent system using Generative Pre-trained Transformers (GPT), specifically GPT-3.5 and GPT-4o, to generate and prioritize user stories from the initial project description. Our experiments conducted on a real-world project show that GPT-3.5 handled complex user stories well, achieving higher semantic similarity scores and ranking them, while GPT-4o performed consistently in prioritizing requirements. These early results show that LLMs perform tasks such as automating certain requirements analysis, particularly generating and prioritizing user stories.