The hard part of AI root cause analysis is no longer the model
The article argues that the main challenge in AI-powered root cause analysis has shifted from building accurate models to solving data quality, integration, and observability issues. While AI models have become more capable, the real difficulty now lies in ensuring reliable, clean, and well-structured data for them to work with effectively.
Background
- Coroot is an open-source observability platform that helps engineers monitor cloud applications and troubleshoot performance issues.
- "Root cause analysis" (RCA) is the process of identifying the underlying reason for a system failure or slowdown — traditionally a slow, manual task for DevOps teams.
- The article argues that the LLM (large language model) part of AI-powered RCA — e.g., asking a chatbot to analyze logs — is now mature. The harder problem is **data quality**: getting clean, correlated, high-cardinality telemetry (metrics, traces, logs) into the model in the first place. Without good inputs, even the best AI returns garbage.
- This reflects a broader shift in the AI-ops space: the bottleneck has moved from model capability to data pipeline engineering.