TL;DR
Bian Que is a flexible agentic framework that improves online system operations by automating skill arrangement and self-evolution, significantly reducing alert volume and resolution time.
Contribution
It introduces a unified operational paradigm, a flexible skill arrangement, and a self-evolving mechanism for online system management using LLMs.
Findings
Reduced alert volume by 75%
Achieved 80% root-cause analysis accuracy
Cut mean time to resolution by over 50%
Abstract
Operating and maintaining (O&M) large-scale online engine systems (eg, search, recommendation and advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. Despite the inherent suitability of LLM-based agents for such operational scenarios, the critical bottleneck impeding their practical deployment lies not in reasoning, but in orchestration capability - specifically, the precise selection of relevant data (encompassing metrics, logs, and change events) and applicable knowledge (including handbook-defined rules and empirically derived practitioner experience) tailored to each individual operational event. Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event-to-(data, knowledge) mapping is intractable under dozens of daily releases. Here we present Bian Que, an agentic operating…
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