Imagine a world where software requirements are no longer a bottleneck, but a catalyst for innovation. A world where tedious analysis is automated, freeing up human experts to focus on what truly matters: building exceptional software. This is the promise of a groundbreaking new approach using AI-powered agents to revolutionize requirements engineering. Traditionally, requirements analysis has been a complex, time-consuming process involving multiple stakeholders, often leading to miscommunication, delays, and costly errors. This new research introduces a multi-agent system where AI models act as virtual product owners, developers, and quality assurance specialists, collaborating to generate, refine, and prioritize user stories. Researchers tested this system with four real-world projects, utilizing powerful LLMs like GPT-3.5, GPT-4, LLaMA3-70, and Mixtral-8B. The results were striking. The AI agents efficiently converted project descriptions into well-defined user stories, assessed their quality against established standards (like INVEST and ISO 29148), and even prioritized them using techniques like WSJF and AHP. Mixtral-8B stood out for its speed, while GPT-3.5 excelled at handling complex stories. Feedback from project teams was overwhelmingly positive, with many praising the system's ability to streamline the entire requirements process. While challenges remain, such as addressing occasional AI 'hallucinations' and integrating more technical details into user stories, this research opens exciting new avenues for AI-driven software development. Future work will focus on refining the agent roles, incorporating retrieval-augmented generation (RAG) to minimize inaccuracies, and exploring even more advanced prioritization methods. This AI-powered approach promises to not just automate requirements analysis but to transform it into a more efficient, accurate, and collaborative process, ultimately leading to better software and faster development cycles.