SiriusHelper: LLM Agent for Big Data Platform Operations
A recent preprint on arXiv (2605.00043) presents SiriusHelper, an operations assistant based on LLM agents tailored for large data platforms. This system tackles real-world deployment issues, such as insufficient scenario coverage for both general inquiries and specific troubleshooting, ineffective knowledge retrieval due to poor multi-hop access and disorganized information, and high maintenance costs stemming from unstructured escalated tickets. Functioning as a comprehensive online assistant, SiriusHelper automatically discerns user intent and directs queries to the correct resolution path, including specialized expert workflows for particular situations. The paper showcases this system as an intelligent assistant deployed for big data platforms, with the goal of alleviating operational challenges and enabling users to quickly access actionable insights.
Key facts
- SiriusHelper is an LLM agent-based operations assistant for big data platforms.
- It addresses limited scenario coverage, inefficient knowledge access, and high maintenance cost.
- The system automatically identifies user intent and routes queries to appropriate handling paths.
- It includes dedicated expert workflows for specialized scenarios.
- The paper is a preprint on arXiv with ID 2605.00043.
- SiriusHelper is described as a deployed intelligent assistant.
- It aims to reduce operational burden and help users find actionable guidance.
- The system uses LLM+RAG but overcomes practical challenges in real deployments.
Entities
Institutions
- arXiv