Intelligent Agents for Requirements Engineering: Use, Feasibility and Evaluation

Large language models (LLMs) have enabled new tools in requirements engineering (RE), often in the form of intelligent agents or virtual assistants. These tools can transform how software engineers perform RE tasks and interact with stakeholders. However, existing research primarily focuses on showcasing the capabilities of these tools rather than their design and evaluation in RE-specific contexts. This limits our understanding of their practical value and hinders broader adoption. To address this gap, we propose a reference model to guide the design, use, and evaluation of intelligent RE agents. Our work introduces new RE use cases, along with evaluation metrics for intelligent RE agents. We present a study design to support systematic development and share early findings demonstrating the feasibility of our approach. The use cases show how agents can add value for RE practitioners, while our synthesized catalogue supports tool evaluation. Finally, our analysis of commercial agents reveals that these tools already support certain aspects of the envisioned RE use cases.