AI-Assisted Root Cause Analysis for Operations
Problem Statement
Operating a 5G Core on Kubernetes generates a huge amount of observability data spread across metrics, logs and traces stores. When something goes wrong, engineers have to manually correlate signals across these different stores to find the root cause, which is slow and requires deep expertise.
Description
Explore how to "connect" LLMs to metrics, logs and traces stores to perform distributed LLM-based debugging: let the LLM query the different observability backends, correlate the signals and propose a root cause.
The idea is also to bring in the Alarms view, so an engineer could tell the LLM "I have an alarm with ID=XYZ" and get back the likely root cause, the correlation with other alerts, and possible remediation steps.
| Aspect | Description |
|---|---|
| Signal access | Connect the LLM to metrics, logs and traces stores |
| Correlation | Correlate signals across stores to spot issues |
| Root cause analysis | Explain what went wrong from an alarm ID or symptom |
| Remediation | Suggest possible remediation steps |
| OTel comparison (bonus) | Compare how well the LLM performs with OTel-compliant signals vs non-OTel ones |
Project Details
Leaders:
- Swisscom (Joel Studler, Alessio Diamanti, Fabian Schulz)
Hackathon Objectives
- Connect an LLM to metrics, logs and traces stores for distributed debugging
- Perform root cause analysis and correlation from an alarm ID
- Suggest remediation steps
- Bonus: compare LLM performance on OTel-compliant vs non-OTel signals