[Context and Motivation] Software engineers can interact with users through digital channels (e.g., online forums) to exchange information about software products and achieve their requirements engineering (RE) goals. However, conducting RE manually is challenging due to the large number of users and the volume of their online feedback. [Question/Problem] Previous work has proposed tools to automatically extract useful information from online feedback (e.g., feature requests); however, these tools suffer from three major limitations: (i) an overlooked RE perspective in their design and evaluation; (ii) insufficient functional and performance capabilities; and (iii) missing evaluations of their ability to address RE needs. [Principal Idea/Results] This paper presents a vision for an intelligent RE software agent designed to overcome these limitations. Specifically, our vision explores how RE can guide the design and evaluation of software agents powered by large language models (LLMs), proposes an empirical assessment of LLMs for RE usage and the agent’s ability to meet RE needs. [Contributions] Our contribution is threefold: (i) a vision for an RE agent, (ii) identification of key challenges, and (iii) a roadmap to address current limitations.