mlsanityData to 7 Oct 2026

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

arXiv 2510.13561 ↗First posted 15 Oct 2025cs.SE cs.AIcited by 1

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Abstract (arXiv)

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/

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

35 of 59 references.

References · 35

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23 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026EviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause Analysiscited by 0

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