GenAIOps on AWS: End-to-End Observability Stack - Part 3

Reading time: ~22-25 minutes Level: Intermediate to Advanced Series: Part 3 of 4 - End-to-End Observability What you'll learn: Build comprehensive observability for GenAI systems with CloudWatch GenAI Observability, X-Ray distributed tracing, and custom metrics The Problem: When GenAI Goes Wrong at 3 AM It's 3 AM. PagerDuty wakes you up: You open your logs. 10,000 lines of JSON. Where do you start? Everything returns 200. But users are complaining. What's actually failing? Is retrieval slow? Can't tell from these logs Is the LLM hallucinating? No quality metrics captured Why is cost 5x higher? Token counts missing Which model is being used? Not tracked What context was retrieved? Lost in the void Traditional observability wasn't built for this. You need GenAI-specific observability that captures the full story: retrieval quality, token consumption, model behavior, and end-to-end traces showing exactly where things break. This is what we're building today. The GenAI Observability Challe