@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://preview.aclanthology.org/display_plenaries/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."
}