From AIOps Anomaly Detection to LLM-Powered RCA: How AI for Incident Response Actually Evolved

Short summary

Traditional AIOps systems successfully detected metric anomalies but couldn't diagnose root causes—they couldn't explain why metrics spiked or connect anomalies to code changes, forcing engineers to manually investigate logs and traces. LLMs fundamentally shift this by processing logs, metrics, code, and traces simultaneously to generate evidence-backed root-cause explanations. However, human judgment remains essential for business context and escalation.

• •AIOps (2018-2022) solved detection but hit a ceiling on diagnosis—couldn't explain why anomalies occurred
• •LLMs enable multi-source reasoning across logs, metrics, code, and traces to generate evidence-backed explanations
• •Human judgment remains critical for business context, novel failures, and escalation decisions

Generated with AI, which can make mistakes.