AGENTIC-SYSTEMSApril 30, 20261 min read7 views

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

Bian Que is an innovative agentic framework designed to automate system operations (O&M), reducing alert volume by 75% and cutting resolution time by half on large-scale platforms like Kuaishou.

Computer Science > Artificial Intelligence

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

Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. While LLM-based agents are a natural fit for these tasks, the deployment bottleneck is not reasoning capability but orchestration: selecting, for each operational event, the relevant data (metrics, logs, change events) and the applicable operational knowledge (handbook rules and practitioner experience). Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event-to-(data, knowledge) mapping is intractable under dozens of daily releases. We present Bian Que, an agentic framework with three contributions: (i) a unified operational paradigm abstracting day-to-day O&M into three canonical patterns: release interception, proactive inspection, and alert root cause analysis; (ii) Flexible Skill Arrangement, where each Skill specifies which data and knowledge to retrieve for a given business-module context and can be automatically generated and updated by LLMs or iteratively refined through natural-language instructions from on-call engineers; (iii) a unified self-evolving mechanism in which one correction signal drives two parallel pathways, case-memory-to-knowledge distillation and targeted Skill refinement. Deployed on the e-commerce search engine of KuaiShou, the major short-video platform in China, Bian Que reduces alert volume by 75%, achieves 80% root-cause analysis accuracy, and cuts mean time to resolution by over 50%. Our framework achieves 99.0% pass rate on offline evaluations. Our code is available at this https URL.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Align AI to Dynamic Human-AI Workflows

Current AI alignment methods fall short by relying on static representations of human preferences. A new paper proposes a paradigm shift toward "interactive and complementary alignment," allowing AI and humans to co-evolve within dynamic workflows.

agentic-systems

MemoHarness: Agent Harnesses That Learn from Experience

Researchers introduce MemoHarness, an adaptive harness optimization framework that enables AI agents to learn from their own executions. By analyzing performance and storing insights in a dual-layer experience bank, it dynamically adapts to new tasks, outperforming static configurations.

agentic-systems

How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment

A new study reveals how AI-powered answer engines are highly vulnerable to hallucination and manipulation when reporting on global conflicts. The lack of robust digital records allows malicious actors to exploit these systems through Generative Engine Optimization (GEO), posing a new threat of AI-driven information warfare.

agentic-systems

AI Agents Do Not Fail Alone:The Context Fails First

A new study validates context-engineering quality as an independent leading indicator of AI agent reliability, introducing an open-source evaluation infrastructure called ProofAgent-Harness to predict behavioral outcomes before deployment.

agentic-systems

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

Researchers have developed LLM-T1D, a novel approach combining reinforcement learning and large language models to automate insulin delivery for Type 1 Diabetes. The system not only outperforms traditional methods but also explains its decisions in plain language, addressing the 'black-box' challenge in medical AI.

agentic-systems

RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

Researchers have introduced RegNetAgents, an AI-oriented multi-agent framework designed to identify regulatory driver candidates across heterogeneous gene networks in cancer genomics. By integrating bulk tumor and single-cell data, the system offers a powerful tool for discovering therapeutic targets in breast and colorectal cancers.