AI Observability

A Production-Readiness Checklist for AI Behavior

Most readiness reviews check whether the service will stay up. An AI feature needs a second pass for what it decides, and for the changes that reach those decisions without a deploy.

Diagnosing an Agent Decision Without an ML Team

Most of what a diagnosis needs isn't model expertise. It's knowing which parts of the explanation were seen, which were said, and which were guessed.

From Trace to Behavior: Instrumenting the Signals That Matter

The Decision-Context Pack: What to Capture Before Something Breaks

An investigation can only use what existed before it started. The way to know what to keep is to run the investigation backwards.

Evaluating AI Behavior Tools: A Buyer's Checklist

Five questions to put to any tool in a category that hasn't standardized yet, and the one artifact that makes a demo tell you something: an incident you already solved.

What to Log When Logs Aren't Enough: A Decision-Context Checklist

Every AI decision loses information the moment it's made. Here's a checklist for deciding what's worth preserving before it's gone.

Behavior Tests That Catch What Evals Miss

Your eval suite grades the output. Here's how to build tests that grade the decision instead, using conditions you can construct this week, no new platform required.

Instrumenting the "Why": Capturing Decision Context in Production

A practical, two-tier way to capture why your AI chose what it did — without rebuilding your stack.