This episode explores how AI agents can streamline requirements analysis in software development. It discusses a study that evaluated the use of large language models (LLMs) in a multi-agent system, featuring four agents: Product Owner (PO), Quality Assurance (QA), Developer, and LLM Manager. These agents collaborate to generate, assess, and prioritize user stories using techniques like the Analytic Hierarchy Process and 100 Dollar Prioritization.The study tested four LLMs—GPT-3.5, GPT-4 Omni, LLaMA3-70, and Mixtral-8B—finding that GPT-3.5 produced the best results. The episode also covers system limitations, such as hallucinations and lack of database integration, and suggests future improvements like using Retrieval-Augmented Generation and expanding agent roles. Overall, the episode highlights the potential of AI agents to revolutionize software requirements analysis.https://arxiv.org/pdf/2409.00038

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