evals
Choosing Your Eval Architecture
The question is not which eval tool. The question is what kind of eval infrastructure your system actually needs. Three architectures, three failure modes, and how they compose into an evidence pipeline.
Drift Detection Patterns for Production Agents
Your agent is still answering. That does not mean it is still behaving the same way. Five drift classes, three detection layers, and the patterns that catch regression before your customers do.
Building an Eval Harness That Survives Production
Most eval harnesses die the same way. Five structural decisions separate the ones that survive production from the ones that quietly rot.
What Should an AI System Actually Prove?
You diagnosed the problem five different ways. Now build the answer. The proof loop: obligation, control, evaluation, evidence, response.
Drift Is the Default
Your agent worked yesterday. That is not a promise about today. Model updates, prompt changes, and shifting inputs cause silent behavioral regression that traditional monitoring doesn't catch.
The Eval Gap: Why Your Agent Works in Staging and Breaks in Production
Your benchmarks are passing. Your agent is failing. Most evals measure isolated performance under controlled conditions while production failure comes from distribution shift, tool-chain errors, and changing reality.