agents
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.
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.
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.
Anatomy of an Evidence Pack
Your system passed the eval. Can you prove it? An evidence pack is a structured, continuously generated collection of artifacts — traces, eval results, approvals, config snapshots, and incident records — that proves your AI system did what you said it would do.
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.
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.
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.