The AI Act became fully applicable August 2, 2026. For high-risk systems, Article 12 requires automatic logging of events over the system's lifetime, with deployer retention of at least six months, and Article 26 flows deployer due-diligence duties down to providers and suppliers. Procurement practice is converging on machine-readable conformity claims (the EU's model contractual clauses for AI procurement are the reference shape).
What a Blueprint offers on that surface is structure, not compliance:
- The claims are machine-checkable. An artifact's capability requirements, operational bounds, classification ceilings, effect intents, and evaluation assertions are typed, registry-validated, digest-covered members — not prose in a PDF. A due-diligence reviewer can verify the exact bytes a producer signed.
- The evidence surface is named honestly. A reconcile result is an
Evidence record whose
not_verifiedset is non-empty by construction — tenancy, live policy, authority, effect ownership, execution, billing, and evaluation truth are structurally never claimed by this protocol. A compliance narrative cannot quote a green verification as more than it is. - Bounds are pre-declared logging triggers. The ceilings members (cost, elapsed time, attempts, fan-out, descendants, tokens) are the operational envelope a logging system watches; the deployment manifest's evaluation binding names the evaluation corpus by digest.
The honest boundary: this protocol does not log anything, satisfy an article, or certify a system. Logging systems do the logging (the IETF's in-progress agent audit-trail work — hash-chained records explicitly mapped to Article 12, SOC 2, and ISO/IEC 42001 — is the candidate substrate); certification remains the registrar's. A blueprint makes the producer's claims verifiable so the human and regulatory machinery has exact, signed inputs to work from.
For the discovery-side counterpart — how a registry's trust manifest relates to blueprint identity — see the ARD mapping.