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

2026-04-29 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, quick

Summary

Bian Que is an agentic framework designed to automate and improve online system operations (O&M) for large-scale engine systems like search, recommendation, and advertising. Developed by Yang Zhao et al. and deployed on KuaiShou's e-commerce search engine, it addresses the challenge of orchestrating relevant data and operational knowledge for LLM-based agents. The framework introduces a unified operational paradigm that categorizes O&M into release interception, proactive inspection, and alert root cause analysis. It features Flexible Skill Arrangement, allowing 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 unified self-evolving mechanism that uses correction signals for case-memory-to-knowledge distillation and targeted skill refinement. This system has reduced alert volume by 75%, achieved 80% root-cause analysis accuracy, and cut mean time to resolution by over 50%, demonstrating a 99.0% pass rate in offline evaluations.

Key takeaway

For AI Architects and Site Reliability Engineers managing large-scale online systems, Bian Que demonstrates a robust approach to automating O&M tasks. You should consider implementing agentic frameworks that emphasize flexible skill orchestration and self-evolving mechanisms to reduce alert fatigue and improve incident resolution times. This can significantly cut operational costs and enhance system stability.

Key insights

Bian Que orchestrates LLM agents for system O&M by dynamically selecting relevant data and knowledge, significantly improving efficiency.

Principles

• Abstract O&M into canonical patterns.
• Dynamically retrieve data/knowledge per context.
• Enable self-evolution via correction signals.

Method

Bian Que uses a unified operational paradigm, Flexible Skill Arrangement for context-specific data/knowledge retrieval, and a self-evolving mechanism for continuous improvement through distillation and skill refinement.

In practice

• Automate alert response with LLM agents.
• Refine operational skills via natural language.
• Distill case memory into knowledge.

Topics

• Agentic Frameworks
• Online System Operations
• LLM-based Agents
• Flexible Skill Arrangement
• Self-Evolving Mechanisms

Code references

• benchen4395/BianQue_Assistant

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

Related on AIssential

• Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations — Bian Que orchestrates LLM agents for online system operations by flexibly arranging skills and enabling self-evolution.
• A Formal Framework for Declarative Agentic AI in Business Process Analysis — A formal framework using AGO methodology and a BPKB enables precise, automated, and sound Agentic AI for business process analysis.
• Article: Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery — Hierarchical multi-agent RAG with autonomous error recovery significantly improves accuracy and reduces hallucinations across structured and unstructured enterprise data.
• From data overload to actionable insights: How Verizon Connect scaled agentic AI to 100,000 users — Agentic AI transforms vast fleet data into actionable insights by dynamically investigating patterns and adapting analysis.
• Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce — eBay's two-agent simulation framework offers controlled, rapid evaluation of conversational shopping assistants, revealing key architectural and LLM judge impacts.
• Building Supercharger: How Rocket Close optimized title operations with agentic AI — Agentic AI solutions can streamline complex, knowledge-intensive workflows by centralizing information and automating research, significantly boosting operational efficiency.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.