Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations

2026-04-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

Bian Que is an agentic framework designed to automate and improve online system operations for large-scale engine systems like search, recommendation, and advertising. It addresses the challenge of orchestrating relevant data and operational knowledge for LLM-based agents in tasks such as release monitoring, alert response, and root cause analysis. The framework introduces a unified operational paradigm that categorizes O&M into release interception, proactive inspection, and alert root cause analysis. A key feature is "Flexible Skill Arrangement," which allows skills to specify data and knowledge retrieval based on business context, with automatic generation/updates by LLMs or refinement via natural language. Bian Que also includes a self-evolving mechanism that distills case memory into knowledge and refines skills. Deployed on KuaiShou's e-commerce search engine, it reduced alert volume by 75%, achieved 80% root-cause analysis accuracy, and cut mean time to resolution by over 50%, with a 99.0% offline evaluation pass rate.

Key takeaway

For AI Scientists and Research Scientists developing LLM-based agents for system operations, Bian Que demonstrates that effective orchestration of data and knowledge, rather than just reasoning capability, is critical for deployment. You should consider implementing flexible skill arrangement and self-evolving mechanisms to adapt to dynamic operational environments and achieve significant improvements in alert reduction and resolution times, as shown by its 75% alert volume reduction and 80% root-cause accuracy.

Key insights

Bian Que orchestrates LLM agents for online system operations by flexibly arranging skills and enabling self-evolution.

Principles

• Abstract O&M into canonical patterns.
• Automate skill generation and refinement.
• Use correction signals for knowledge distillation.

Method

Bian Que employs a unified operational paradigm, Flexible Skill Arrangement for data/knowledge retrieval, and a self-evolving mechanism for continuous improvement through case-memory distillation and targeted skill refinement.

In practice

• Reduce alert volume by 75%.
• Improve root-cause analysis accuracy to 80%.
• Cut mean time to resolution by over 50%.

Topics

• Bian Que Framework
• Online System Operations
• LLM-based Agents
• Flexible Skill Arrangement
• Root Cause Analysis

Code references

• benchen4395/BianQue_Assistant

Best for: AI Scientist, Research Scientist, MLOps Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.