OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918312619802624 |
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| author | Di, Peng Chen, Faqiang Bai, Xiao Yang, Hongjun Li, Qingfeng Wei, Ganglin Mou, Jian Shi, Feng Chen, Keting Tang, Peng Shen, Zhitao Li, Zheng Shi, Wenhui Guo, Junwei Yu, Hang |
| author_facet | Di, Peng Chen, Faqiang Bai, Xiao Yang, Hongjun Li, Qingfeng Wei, Ganglin Mou, Jian Shi, Feng Chen, Keting Tang, Peng Shen, Zhitao Li, Zheng Shi, Wenhui Guo, Junwei Yu, Hang |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies Di, Peng Chen, Faqiang Bai, Xiao Yang, Hongjun Li, Qingfeng Wei, Ganglin Mou, Jian Shi, Feng Chen, Keting Tang, Peng Shen, Zhitao Li, Zheng Shi, Wenhui Guo, Junwei Yu, Hang Software Engineering Artificial Intelligence 68N30 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/ |
| title | OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies |
| topic | Software Engineering Artificial Intelligence 68N30 |
| url | https://arxiv.org/abs/2510.13561 |