This work explores the automated elicitation of functional software requirements using Large Language Models (LLMs). The proposed approach employs multiple LLMs in conjunction with Retrieval-Augmented Generation (RAG) across iterative refinement rounds. This process enables the generation of candidate requirements from high-level software descriptions, with validation through similarity analysis. The method addresses key challenges in current AI-based elicitation approaches, including lack of iterative refinement, hallucination control, and semantic convergence. Preliminary results demonstrate that multi-round feedback and cross-model integration improve alignment with expert-defined requirements. This study marks the initial phase of a broader doctoral research project titled Agent Family for Software Engineering Teams, which aims to develop a set of intelligent agents to support various stages of the software engineering lifecycle, including requirement elicitation, project management, and software quality, ultimately forming a cohesive and responsible AI ecosystem for software teams.