@inproceedings{barrionuevo-valenzuela-etal-2026-supporting,
    title = "Supporting human operators during customer service interactions with agentic-{RAG}",
    author = "Barrionuevo-Valenzuela, Juan  and
      Calder{\'o}n-Gonz{\'a}lez, Daniel  and
      Callejas, Zoraida  and
      Griol, David",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.35/",
    pages = "348--356",
    abstract = {This paper focuses on improving customer service in call centers, where finding accurate answers in the shortest possible time is crucial. The proposed solution is the development of a conversational {AI} system that acts as a ``copilot'' for human operators. The main goal of this copilot is to assist the operator in real time by providing conversation summaries, relevant domain information, and suggested responses that help guide the interaction toward a successful resolution. To achieve this, different approaches to Retrieval Augmented Generation ({RAG}) have been explored. The proposed agentic-{RAG} architecture integrates multiple autonomous agents for routing, retrieval validation, and response generation, achieving consistent improvements in real-time performance, grounding, and overall user experience across diverse service scenarios. Empirical results with the Action-Based Conversations Dataset ({ABCD}) corpus show that the use of agents proved to be effective in handling unstructured conversational data. The proposed approach showed an improvement in the quality, relevance, and accuracy of the generated responses with respect to a na{\"i}ve {RAG} baseline. It is important to emphasize that this system is not intended to replace the operator, but rather to act as a support tool to enhance efficiency and customer satisfaction.}
}