Admissible Execution: Invariants for AI Execution Authority

As AI systems move from experimentation into production, governance failures increasingly occur at execution time, when AI recommendations turn into real actions under operational pressure. While many AI governance efforts focus on policies, oversight processes, and post-hoc auditability, these approaches break down under scrutiny. Auditors and regulators do not ask whether governance existed; they ask whether governance held. This paper introduces admissible execution as the minimum bar for responsible AI-assisted execution. Admissibility requires that execution authority can be proven under adversarial review, independent of operator testimony, mutable logs, or reconstructed narratives. The paper defines a set of non-negotiable execution invariants that must hold for authority, accountability, and non-repudiation to survive audit, incident review, and regulatory challenge. It also enumerates common non-admissible patterns that repeatedly fail in real-world systems despite appearing compliant during normal operation. This is a position paper, not an implementation, protocol, or compliance guide. It is vendor-neutral and framework-agnostic. Its purpose is to establish a shared, defensible standard for execution-time authority in AI-driven systems.