Generative AI for Observability: Automating Root Cause Analysis in Modern IT Systems

As modern IT systems grow in complexity, the traditional approaches to root cause analysis (RCA) are proving too slow, too manual, and too reactive. Observability platforms produce a flood of logs, metrics, and traces—but transforming that raw telemetry into actionable insights remains a major challenge. This session explores how Generative AI can be used to automate RCA by understanding system behavior, correlating disparate signals, and generating human-readable incident reports. By leveraging pre-trained language models and retrieval-augmented architectures, AI agents can now analyze incidents contextually and assist engineers in identifying probable root causes—dramatically reducing mean time to resolution (MTTR). Key takeaways: How to design observability workflows enhanced with generative reasoning. Techniques for summarizing, correlating, and interpreting log and metric data using AI. Real-world architecture for integrating Generative AI into operational pipelines (e.g., prompt templates, embeddings, RAG).