Production controls for consequential AI

Reliability is an architecture, not a promise.

Describe the deployment. Get a risk-sensitive control stack, test its failure paths, and take the evidence with you.

Grounded in the production challenge shared at VivaTech 2026

Deployment scenario

Set the consequence boundary

Saved locally

Your control plane

A layered path to a safe outcome

Computed tier Critical High consequence, action autonomy, restricted data
Government benefits agent 6 controls active
Evaluation gate PASS

3 of 3 outcomes fail safely.

What this proves

The architecture routes uncertainty to abstention or a human decision. It does not guarantee model correctness. It makes failure behavior explicit and testable.

Failure-path evidence

Every outcome needs somewhere safe to go.

Mark representative outcomes. The gate passes only when none can leave the system as an unsafe answer or action.

A

Answer only when retrieval and policy checks agree.

B

Stop rather than resolve uncertainty with invention.

C

Require accountable human approval before execution.

Take the architecture with you

Turn the control stack into a build review.

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