The DevOps Root Cause Analysis Agent is an AI-driven tool that cuts through the noise of modern observability data to pinpoint the root cause of production incidents. By correlating signals across logs, metrics, and distributed traces, the agent surfaces actionable hypotheses ranked by confidence — so your engineers spend time fixing problems, not hunting for them.Documentation Index
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Quickstart
Get the agent running and perform your first root cause analysis in minutes.
How It Works
Understand the AI pipeline: signal ingestion, correlation, and hypothesis ranking.
Configuration
Set up environment variables, integrations, and agent behavior settings.
Guides
Step-by-step walkthroughs for common analysis and integration workflows.
What the Agent Does
When an incident fires, the DevOps Root Cause Analysis Agent:Ingests Signals
Pulls logs, metrics, and traces from connected data sources — Prometheus, Elasticsearch, Jaeger, and more — for the incident time window.
Correlates Events
Uses an AI model to identify anomalies, temporal correlations, and causal relationships across all ingested signals simultaneously.
Ranks Hypotheses
Generates a ranked list of root cause hypotheses, each with supporting evidence drawn directly from your observability data.
Key Capabilities
Multi-Signal Correlation
Combines logs, metrics, and traces into a unified view for holistic incident analysis.
AI Hypothesis Ranking
Ranks root cause candidates by confidence score using LLM-based reasoning over your data.
Interactive Dashboard
Explore findings and drill into evidence through a Streamlit-powered investigation UI.
Async Processing
Celery and Redis back the analysis pipeline for non-blocking, scalable task execution.
Pluggable Connectors
Add custom data source connectors to pull from any observability or logging platform.
Alerting Integration
Push analysis summaries to PagerDuty, Slack, or any webhook-compatible endpoint.