Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
In the rapidly evolving domain of online system operations, the need for effective management and oversight of large-scale systems such as search engines, recommendation algorithms, and advertising platforms has never been more pressing. The recent introduction of the Bian Que framework, as detailed in the preprint article arXiv:2604.26805v1, promises to significantly enhance operational efficiency in these areas.
Abstract of Bian Que Framework
Operating and maintaining large-scale online engine systems requires substantial human effort for tasks such as release monitoring, alert response, and root cause analysis. Traditional approaches often struggle under the weight of overwhelming data and operational complexities. While large language model (LLM)-based agents are ideal candidates for these tasks, the main challenge lies not in their reasoning capabilities but in effective orchestration. This involves selecting relevant data—such as metrics, logs, and change events—and applicable operational knowledge, including handbook rules and practitioner experience.
Feeding all available signals into the system without discernment can lead to data dilution and hallucination, while manually curating the event-to-data and knowledge mapping becomes impractical amid the numerous daily releases. The Bian Que framework addresses these challenges through three key contributions:
- Unified Operational Paradigm: This concept abstracts day-to-day operations and maintenance into three canonical patterns: release interception, proactive inspection, and alert root cause analysis.
- Flexible Skill Arrangement: Each Skill within the framework specifies which data and knowledge to retrieve based on a given business-module context. These Skills can be automatically generated and updated by LLMs or refined iteratively through natural-language instructions from on-call engineers.
- Unified Self-Evolving Mechanism: This mechanism allows for one correction signal to drive two parallel pathways: case-memory-to-knowledge distillation and targeted Skill refinement.
Real-World Impact and Deployment
Deployed on KuaiShou’s e-commerce search engine—one of China’s leading short-video platforms—Bian Que has demonstrated remarkable results. The framework has successfully reduced alert volume by 75%, achieved an impressive 80% accuracy in root cause analysis, and cut the mean time to resolution by over 50%. Furthermore, Bian Que has shown a 99.0% pass rate in offline evaluations, underscoring its effectiveness and reliability in real-world applications.
The implications of such advancements are significant. By streamlining operations and enhancing the accuracy of responses to operational events, organizations can not only reduce operational costs but also improve service quality, thereby enhancing user satisfaction and engagement. The Bian Que framework exemplifies how innovative AI solutions can transform the operational landscape of online systems.
Accessing the Code
For those interested in exploring the Bian Que framework further, the code is available for public access at https://github.com/benchen4395/BianQue_Assistant. Researchers and engineers are encouraged to delve into the framework’s functionalities and potentially adapt it for their specific operational needs.
As AI continues to evolve, frameworks like Bian Que will play a vital role in shaping the future of online system operations, driving efficiency, and enhancing the overall user experience.
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