Architectural Laws And Derived Constraints Of Verification And Consequential AI Reliance

AI systems are becoming more capable, better at self-critique and increasingly composed into agentic systems. This paper asks what does not change simply because capability improves. It tests six candidate propositions and demotes two of its own. Four qualify as Architectural Laws: Verification Non-Emergence, Generative Non-Authentication, Verification Locality and Compositional Non-Uplift. Two remain necessary but require additional evidential or institutional conditions and are reclassified as Derived Constraints: Evidential Attachment and Reliance-Boundary Proof Dependency. For agentic systems, Compositional Non-Uplift is critical: repetition, debate, model judging and multi-agent coordination do not create Verification through composition alone. More checks can improve performance or confidence without creating independent evidential authority. Scaling an architecture feels like adding assurance. It adds boundaries. This is not an argument that AI cannot be verified or used consequentially. A wider system can contain genuine bounded Verification. The question is where it resides, what proposition it governs and what evidence supports its authority. For designers, governance and assurance teams, the paper converts that architecture into ten implementation-neutral requirements. Increasing architectural scale multiplies Verification boundaries. It does not dissolve them.