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PRAXIS system uses LLMs to automate root-cause analysis of cloud incidents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PRAXIS, a new system designed to diagnose and resolve cloud incidents caused by code or configuration errors. PRAXIS utilizes an LLM-driven approach to traverse service dependency graphs and program dependence graphs, enabling more accurate root-cause analysis. In evaluations, PRAXIS demonstrated a significant improvement in accuracy compared to existing methods while also reducing computational resource usage. AI

IMPACT Introduces a novel LLM-driven approach for automated root-cause analysis, potentially reducing cloud incident resolution time and cost.

RANK_REASON This is a research paper introducing a new system for root-cause analysis in cloud incidents.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PRAXIS system uses LLMs to automate root-cause analysis of cloud incidents

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This is a research paper introducing a new system for root-cause analysis in cloud incidents.
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shengkun Cui, Rahul Krishna, Saurabh Jha, Ravishankar K. Iyer ·

    PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis

    arXiv:2512.22113v2 Announce Type: replace-cross Abstract: Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and deploys an agentic workflow for diagnosing code- and configuration-caused cloud inc…

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