3½ Questions to Make
Observability AI-Ready
A practical framework to make observability data usable for AI reasoning and automation, not just dashboards.
Most teams approach observability with a simple idea: collect everything now and decide what matters later. It feels safe, but it creates noise and slows diagnosis when something breaks (for humans and AI).
This guide explains why “more telemetry” does not produce more understanding, and introduces The Who Cares Framework, a top-down method for deciding what telemetry actually matters by working backward from customer outcomes to service behavior. You will take away what you need to do to make your observability AI-ready.
In this paper, you'll learn:
- Why “collect everything, decide later” just creates more noise
- How to make telemetry intentional by tying it to customer interactions and outcomes
- Why traces come first, and how they provide the linkage that makes metrics actionable and logs usable
- How to define metrics as deviation detectors, not vanity time series
- Why semantic conventions are the foundation of reliable cross-service reasoning
- How you can make your observability stack AI-ready
Get the Guide
Written for Platform & SRE Teams
Trusted by teams who can't afford downtime
