No Escape: Mathematical Containment for Artificial Agents

We present the : a formal result showing that AI systems operating under properly implemented structural constraints cannot circumvent ethical requirements through reasoning, reinterpretation, or representation manipulation---regardless of their intelligence level. Unlike behavioral approaches that specify an AI should do (leaving room for creative circumvention), structural approaches define the within which outputs exist. We prove that the combination of (1) canonicalization to equivalence-class normal forms, (2) physical grounding of evaluations in measurable observables, and (3) mandatory audit artifacts creates a containment architecture with no formal escape route. The only attacks on such a system are physical (sensor manipulation) or political (refusing to mandate compliance)---not cognitive. This reframes AI alignment from ``making AI want the right things'' to ``making AI outputs structurally constrained regardless of intent.'' We present the core theorems, analyze purported escape routes, and discuss implications for AI governance. Author preprint deposited for archival and citation. Draft — pending author review.