safety
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.
Mapping the EU AI Act to Engineering Evidence
The regulation tells you what to prove. It does not tell you how to build the proof. This essay maps every major obligation from the EU AI Act to a specific control, eval, and evidence artifact.
Controls Are Not Guardrails
A guardrail catches the output. A control proves the system works. The difference is the evidence layer — obligation, mechanism, eval, evidence, owner.
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.
Who Owns the Agent's Mistake?
The legal answer is converging fast. Courts are rejecting the 'AI did it' defense. The question is whether your organization has the infrastructure to assign accountability when an agent fails.
Guardrails Are Not Safety
Boundary guardrails are the AI equivalent of locking the front door while leaving the windows open. Real safety requires observability, containment, least privilege, and structured human review.
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.