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arXiv · 2510.13561

OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies

Peng Di·Faqiang Chen·Xiao Bai·Hongjun Yang·Qingfeng Li·Ganglin Wei·Jian Mou·Feng Shi·Keting Chen·Peng Tang·Zhitao Shen·Zheng Li·Wenhui Shi·Junwei Guo·Hang Yu
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Abstract

The escalating complexity of modern software imposes an unsustainable operational burden on Site Reliability Engineering (SRE) teams, demanding AI-driven automation that can emulate expert diagnostic reasoning. Existing solutions, from traditional AI methods to general-purpose multi-agent systems, fall short: they either lack deep causal reasoning or are not tailored for the specialized, investigative workflows unique to SRE. To address this gap, we present OpenDerisk, a specialized, open-source multi-agent framework architected for SRE. OpenDerisk integrates a diagnostic-native collaboration model, a pluggable reasoning engine, a knowledge engine, and a standardized protocol (MCP) to enable specialist agents to collectively solve complex, multi-domain problems. Our comprehensive evaluation demonstrates that OpenDerisk significantly outperforms state-of-the-art baselines in both accuracy and efficiency. This effectiveness is validated by its large-scale production deployment at Ant Group, where it serves over 3,000 daily users across diverse scenarios, confirming its industrial-grade scalability and practical impact. OpenDerisk is open source and available at https://github.com/derisk-ai/OpenDerisk/

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Peng Di, Faqiang Chen, Xiao Bai, Hongjun Yang, Qingfeng Li, Ganglin Wei, Jian Mou, Feng Shi, Keting Chen, Peng Tang, Zhitao Shen, Zheng Li, Wenhui Shi, Junwei Guo, Hang Yu. 2025-10-15. OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies. https://doi.org/10.1145/3786583.3786864

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