reliability
The Evidence Plane for AI Systems
The missing layer between what your system must prove and how your organization proves it. A framework synthesis connecting obligations, controls, evaluations, evidence artifacts, and the response loop.
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.
The Incident Response Gap in AI Systems
You built the controls. You still cannot contain the failure. Most organizations have started building AI controls. Far fewer have built AI incident 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.
Agent Failures Are Distributed Systems Failures
You already have the mental models for agent reliability. Retries, circuit breakers, observability — the vocabulary changes, the physics don't.