This paper proposes the adaptation of Retrieval Augmented Generation (RAG) based systems into Agile Software Development workflows, addressing the need for specialized evaluation methods that align with Agile’s iterative, feedback driven nature. Through a systematic mapping study of 20 papers, we identify existing evaluation frameworks for RAG systems and explore how they can be applied within Agile settings. The research highlights the role of Evaluator-Agents in aligning the responsiveness of RAG systems, with a focus on Multi-agent AI systems that are more efficient at handling complex, distributed tasks than Single-agent systems. The findings contribute to identify the potential of RAG and its evaluation in facilitating real time feedback and continuous improvement for Agile development.