@inproceedings{park-etal-2025-practical,
title = "A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs",
author = "Park, Chiwan and
Jang, Wonjun and
Kim, Daeryong and
Ahn, Aelim and
Yang, Kichang and
Hwang, Woosung and
Roh, Jihyeon and
Park, Hyerin and
Wang, Hyosun and
Kim, Min Seok and
Kang, Jihoon",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.107/",
doi = "10.18653/v1/2025.acl-industry.107",
pages = "1508--1519",
ISBN = "979-8-89176-288-6",
abstract = "The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications.However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints.This can be seen as two conflicting requirements due to the probabilistic nature of LLMs.In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications.We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations.Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents."
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<abstract>The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications.However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints.This can be seen as two conflicting requirements due to the probabilistic nature of LLMs.In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications.We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations.Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents.</abstract>
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%0 Conference Proceedings
%T A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs
%A Park, Chiwan
%A Jang, Wonjun
%A Kim, Daeryong
%A Ahn, Aelim
%A Yang, Kichang
%A Hwang, Woosung
%A Roh, Jihyeon
%A Park, Hyerin
%A Wang, Hyosun
%A Kim, Min Seok
%A Kang, Jihoon
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F park-etal-2025-practical
%X The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications.However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints.This can be seen as two conflicting requirements due to the probabilistic nature of LLMs.In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications.We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations.Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents.
%R 10.18653/v1/2025.acl-industry.107
%U https://aclanthology.org/2025.acl-industry.107/
%U https://doi.org/10.18653/v1/2025.acl-industry.107
%P 1508-1519
Markdown (Informal)
[A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs](https://aclanthology.org/2025.acl-industry.107/) (Park et al., ACL 2025)
ACL
- Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, and Jihoon Kang. 2025. A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1508–1519, Vienna, Austria. Association for Computational Linguistics.