Use one AI-built fixture

Prepare one small synthetic CRUD application with a named source revision. The fixture should include a search, create, edit, denied action, bounded failure, and recovery state. Give every candidate the same test data, named operator, named reviewer, and source-date cutoff.

Admission control Required evidence
Deployment identity Version, artifact, environment, actor, and timestamp
Change authority Named create, review, and promotion permissions
Runtime boundary Application, data source, credential, and integration map
Failure handling Bounded error, alert, recovery owner, and evidence path
Recovery boundary Application rollback separate from business-data recovery
Handoff A second operator finds the runbook, evidence, and unresolved work

Every control needs a record from the evaluated environment. When a product cannot expose the same condition, call the comparison ineligible rather than using marketing copy to fill the gap.

Make admission precede ranking

The candidate that looks quickest in a demonstration may not have enough evidence for the intended operator. Conversely, a candidate with a more involved setup may be the only one the team can observe under the required ownership model. The admission card does not decide that tradeoff; it makes the premises visible for the platform owner.

Use the promotion and rollback method to keep revision recovery separate from business-data recovery. The maintenance ownership comparison provides a separate ownership vocabulary, while platforms for publishing AI-built internal apps should not be read as a ranking for a new workload.

Stop condition

Do not claim that a candidate is production-ready, secure, reliable, least costly, or best. Stop if any candidate lacks a comparable environment, operator role, release record, data-recovery boundary, or current evidence. The admission table above currently contains no observed product result.

Can an AI-built app pass this method without a production deployment?

It can be evaluated in an authorised non-production environment. That does not prove its production behavior; it creates the evidence needed to decide whether to proceed.