AI-Incident-Root-Cause-Analyzer
An open-source multi-agent AI framework for root cause analysis that decomposes fault investigation into three independent agent stages: log parsing, hypothesis generation, and evidence validation. It produces a root cause report that includes confidence scores and recommended follow-up steps.
Timeline (last 90 days)
AI-Incident-Root-Cause-Analyzer is an open-source project developed by Vikram Boddupalli that adopts a multi-agent root cause analysis (RCA) architecture.
This multi-agent RCA architecture splits the investigation process into three agents: a Log Parser, a Hypothesis Generator, and an Evidence Validator.
In this case, the JSON parsing exception is most likely merely a symptom of the failure rather than the root cause itself
When evidence is absent, AI systems should honestly convey uncertainty rather than manufacture false certainty
This multi-agent RCA approach is applicable to scenarios such as CI/CD pipeline failures, Kubernetes startup and readiness issues, API authentication and authorization failures, cross-environment configuration drift, dependency failures, and automated test failures
The greatest advantage of this architecture over black-box single-pass diagnosis is traceability: engineers can trace back the entire investigation chain to determine whether the problem lies in the extraction, hypothesis, or validation stage
All Facts (6)
AI-Incident-Root-Cause-Analyzer is an open-source project developed by Vikram Boddupalli that adopts a multi-agent root cause analysis (RCA) architecture.
50%UnverifiedThis multi-agent RCA architecture splits the investigation process into three agents: a Log Parser, a Hypothesis Generator, and an Evidence Validator.
50%UnverifiedIn this case, the JSON parsing exception is most likely merely a symptom of the failure rather than the root cause itself
50%UnverifiedWhen evidence is absent, AI systems should honestly convey uncertainty rather than manufacture false certainty
50%UnverifiedThis multi-agent RCA approach is applicable to scenarios such as CI/CD pipeline failures, Kubernetes startup and readiness issues, API authentication and authorization failures, cross-environment configuration drift, dependency failures, and automated test failures
50%UnverifiedThe greatest advantage of this architecture over black-box single-pass diagnosis is traceability: engineers can trace back the entire investigation chain to determine whether the problem lies in the extraction, hypothesis, or validation stage
50%