@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://aclanthology.org/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.}
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<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 naïve 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.</abstract>
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%0 Conference Proceedings
%T Supporting human operators during customer service interactions with agentic-RAG
%A Barrionuevo-Valenzuela, Juan
%A Calderón-González, Daniel
%A Callejas, Zoraida
%A Griol, David
%Y Riccardi, Giuseppe
%Y Mousavi, Seyed Mahed
%Y Torres, Maria Ines
%Y Yoshino, Koichiro
%Y Callejas, Zoraida
%Y Chowdhury, Shammur Absar
%Y Chen, Yun-Nung
%Y Bechet, Frederic
%Y Gustafson, Joakim
%Y Damnati, Géraldine
%Y Papangelis, Alex
%Y D’Haro, Luis Fernando
%Y Mendonça, John
%Y Bernardi, Raffaella
%Y Hakkani-Tur, Dilek
%Y Di Fabbrizio, Giuseppe ”Pino”
%Y Kawahara, Tatsuya
%Y Alam, Firoj
%Y Tur, Gokhan
%Y Johnston, Michael
%S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
%D 2026
%8 February
%I Association for Computational Linguistics
%C Trento, Italy
%F barrionuevo-valenzuela-etal-2026-supporting
%X 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 naïve 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.
%U https://aclanthology.org/2026.iwsds-1.35/
%P 348-356
Markdown (Informal)
[Supporting human operators during customer service interactions with agentic-RAG](https://aclanthology.org/2026.iwsds-1.35/) (Barrionuevo-Valenzuela et al., IWSDS 2026)
ACL