SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms
Yu Shen, Shiyang Liu, Qihang He, Yihang Cheng, Haining Xie, Zhiming He, Huahua Fan, Xianzhi Tan, Teng Ma, Shaoquan Zhang, Danqing Huang, Fan Jiang, Yang Li, Chongqing Zhao, Peng Chen, Jie Jiang, Bin Cui

TL;DR
SiriusHelper is an intelligent assistant for big data platforms that improves troubleshooting, reduces operational workload, and continuously learns from tickets through multi-hop retrieval and SOP distillation.
Contribution
It introduces a unified, deployed LLM-based assistant with hierarchical knowledge management and automated SOP extraction for enhanced troubleshooting and maintenance.
Findings
Reduces online ticket volume by 20.8%.
Outperforms alternative solutions in deployment.
Enables multi-hop retrieval with improved reliability.
Abstract
Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly find actionable guidance and reduce operational burden. While recent LLM+RAG assistants provide a natural interface, they face practical challenges in real deployments: limited scenario coverage across both general consultation and domain-specific troubleshooting workflows, inefficient knowledge access due to inadequate multi-hop retrieval and flat knowledge organization, and high maintenance cost because escalated tickets are unstructured and hard to convert into assistant improvements and reusable SOPs. In this paper, we present SiriusHelper, a deployed intelligent assistant for big data platforms. SiriusHelper serves as a unified online assistant that automatically identifies user intent and routes queries to the right handling path,…
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