LLM-Powered Multi-Agent Collaboration for Intelligent Industrial On-Call Automation
This program is tentative and subject to change.
In large-scale enterprises, on-call engineers (OCEs) are critical for ensuring service availability and reliability. However, as incidents grow in volume and complexity, traditional manual on-call processes are becoming increasingly inadequate. Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and multi-agent collaboration, presenting new opportunities for automation. We propose OncallX, an end-to-end automated on-call system designed for real-world industrial scenarios that integrates LLMs with multi-agent cooperation to enable intelligent and efficient incident management. OncallX first enhances user queries by leveraging external knowledge bases and multi-turn dialogue interactions. Subsequently, multiple expert agents collaborate through tree-search-based mechanisms to generate effective responses and solutions. When incidents cannot be resolved automatically, OncallX accurately assigns them to the most appropriate teams. Comprehensive experiments conducted in the real-world production environment of a top-tier global online video service provider demonstrate that OncallX efficiently responds to incidents and accurately triages tickets, significantly outperforming existing methods in both automated metrics and human evaluations. Furthermore, OncallX has been successfully deployed in production for two months, during which it has substantially enhanced on-call efficiency, reducing average incident response time to just 21 seconds and average triage time to 4 seconds—representing a transformative improvement in operational excellence.
This program is tentative and subject to change.
Mon 17 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
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11:30 10mTalk | Why Do Machine Learning Notebooks Crash? An Empirical Study on Public Python Jupyter Notebooks Journal-First Track Yiran Wang Linköping University, Willem Meijer Linköping University, José Antonio Hernández López Universidad de Murcia, Ulf Nilsson Linköping University, Daniel Varro Linköping University / McGill University | ||
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11:50 10mTalk | LLM-Powered Multi-Agent Collaboration for Intelligent Industrial On-Call Automation Research Papers Ruowei Fu Nankai University, Yang Zhang ByteDance Inc., Zeyu Che Nankai University, Xin Wu ByteDance Inc., Zhenyu Zhong Nankai University, Zhiqiang Ren ByteDance Inc., Shenglin Zhang Nankai University, Feng Wang ByteDance Inc., Yongqian Sun Nankai University, Xiaozhou Liu ByteDance Inc., Kexin Liu Nankai University, Yu Zhang ByteDance Inc. | ||
12:00 10mTalk | SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking Research Papers Zewei Lin Sun Yat-sen University, Jiachi Chen Sun Yat-sen University, Jingwen Zhang School of Software Engineering, Sun Yat sen University, Zexu Wang Sun Yat-sen University, Yuming Feng Peng Cheng Laboratory, Weizhe Zhang Harbin Institute of Technology, Zibin Zheng Sun Yat-sen University | ||
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12:20 10mTalk | LineBreaker: Finding Token-Inconsistency Bugs using Large Language Models Research Papers Hongbo Chen Indiana University Bloomington, Yifan Zhang San Diego State University, Xing Han The Hong Kong University of Science and Technology, Tianhao Mao Indiana University, Huanyao Rong Indiana University Bloomington, Yuheng Zhang Tsinghua University, Hang Zhang Indiana University, XiaoFeng Wang ACM member, Luyi Xing Indiana University Bloomington/University of Illinois Urbana-Champaign, Xun Chen Samsung Research America | ||