@inproceedings{zhao-etal-2025-agent,
title = "Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in {LLM}-based Customer Support",
author = "Zhao, Cen and
Zhang, Tiantian and
Su, Hanchen and
Zhang, Yufeng and
Su, Shaowei and
Xu, Mingzhi and
Liu, Yu and
Han, Wei and
Werner, Jeremy and
Cheng, Claire Na and
Mehdad, Yashar",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.135/",
pages = "1919--1930",
ISBN = "979-8-89176-333-3",
abstract = "We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. These feedback signals seamlessly feed back into models' updates, reducing retraining cycles from months to weeks. Our production pilot involving US-based customer support agents demonstrated significant improvements in retrieval accuracy (+11.7{\%} recall@75, +14.8{\%} precision@8), generation quality (+8.4{\%} helpfulness) and agent adoption rates (+4.5{\%}). These results underscore the effectiveness of embedding human feedback loops directly into operational workflows to continuously refine LLM-based customer support system."
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%0 Conference Proceedings
%T Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support
%A Zhao, Cen
%A Zhang, Tiantian
%A Su, Hanchen
%A Zhang, Yufeng
%A Su, Shaowei
%A Xu, Mingzhi
%A Liu, Yu
%A Han, Wei
%A Werner, Jeremy
%A Cheng, Claire Na
%A Mehdad, Yashar
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zhao-etal-2025-agent
%X We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. These feedback signals seamlessly feed back into models’ updates, reducing retraining cycles from months to weeks. Our production pilot involving US-based customer support agents demonstrated significant improvements in retrieval accuracy (+11.7% recall@75, +14.8% precision@8), generation quality (+8.4% helpfulness) and agent adoption rates (+4.5%). These results underscore the effectiveness of embedding human feedback loops directly into operational workflows to continuously refine LLM-based customer support system.
%U https://aclanthology.org/2025.emnlp-industry.135/
%P 1919-1930
Markdown (Informal)
[Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support](https://aclanthology.org/2025.emnlp-industry.135/) (Zhao et al., EMNLP 2025)
ACL
- Cen Zhao, Tiantian Zhang, Hanchen Su, Yufeng Zhang, Shaowei Su, Mingzhi Xu, Yu Liu, Wei Han, Jeremy Werner, Claire Na Cheng, and Yashar Mehdad. 2025. Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1919–1930, Suzhou (China). Association for Computational Linguistics.