Fail-closed AI: refuse when evidence is missing
Implement fail-closed behavior with required evidence, strict schemas, timeouts and explicit refusals.
Fail-closed means missing evidence, ambiguity or control failure never becomes implicit permission.
A permissive fallback turns a model, retrieval or verifier outage into uncontrolled action.
Refusal conditions must be tested as first-class behaviors, not added after implementation.
What the architecture must make explicit
Capability does not grant authority. Critical systems must separate what proposes, verifies, permits and executes.
Move from a keyword to an inspectable engineering decision
Separate
Decouple probabilistic proposal, validation, permission and physical execution.
Bound
Limit tools, data, actions, duration and environments available to each component.
Refuse
Make missing evidence, timeouts and ambiguity explicit states.
Audit
Retain the applied rule, its inputs, version and resulting decision.
A claim is only as strong as its binding
The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.
Questions that change the design
What does fail-closed ai: refuse when evidence is missing require?
Fail-closed means missing evidence, ambiguity or control failure never becomes implicit permission. Capability does not grant authority. Critical systems must separate what proposes, verifies, permits and executes.
Which evidence should be retained?
At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. Refusal conditions must be tested as first-class behaviors, not added after implementation.
Does AMF prescribe a particular model?
No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.