LLM Incident Correlation
Real-time synthesis of incident signals
Overview
During incidents, connect the dots across deploys, commits, alerts, and logs in real-time. AI synthesizes multiple data streams to surface root cause faster than manual investigation.
Why It Matters
The answer exists across multiple systems. AI connects deploys, commits, alerts, and logs instantly - finding patterns humans miss under pressure.
The Risk
Mean time to resolution (MTTR) is determined by how fast you connect the dots. Siloed data means repeated incidents, longer outages, and exhausted on-call teams. The information exists - you just can't find it fast enough.
Implementation Components
A complete implementation of this capability includes:
- Integration with deploy tracking, monitoring, logs
- Real-time incident data aggregation
- LLM pattern recognition across data sources
- Temporal correlation (what changed before the incident)
- Root cause hypothesis generation
- Slack/PagerDuty integration for incident response
AI Integration
This capability leverages AI/LLM technology to enhance its functionality.
Trigger
Alert fires or incident declared
Input
Recent deploys + error logs + metrics + alerts
Output
Correlation analysis + root cause hypotheses + related incidents
Implementation Pattern
- 1Aggregate incident signals (deploys, logs, alerts)
- 2Send to LLM with incident context
- 3Identify correlation patterns
- 4Generate root cause hypotheses
Pipeline Coverage
This continuous capability monitors and applies to the following pipeline phases:
Tool Examples
These are examples, not endorsements. Choose what fits your context.
Dependencies
This capability stands independently.
Same Layer
Other capabilities in this continuous layer
- •#30 Central Logging
- •#31 LLM Log Analysis
- •#32 Metrics & Alerting
- •#33 Error Tracking
- •#34 LLM Error Analysis
+10 more