Autonomy as a Constraint: The Tri-Axiom Compliance Standard for Artificial Intelligence

Contemporary approaches to AI alignment and governance primarily rely on probabilistic optimization, preference learning, or value-based constraints. These approaches implicitly assume that undesirable behavior can be mitigated through improved objective specification or training, an assumption that becomes increasingly fragile as system capability scales. This paper proposes the Tri-Axiom Compliance Standard (TACS), a constraint-based framework that governs artificial intelligence at the level of external interaction, rather than internal motivation or ethical reasoning. TACS defines three invariant axioms—Autonomy > Coercion, Reaction > Initiation, and Equality > Hierarchy—which operate as non-negotiable veto conditions on permissible AI actions. These axioms are formalized as partial-order constraints that prohibit coercive, pre-emptive, or asymmetrically privileged behavior irrespective of predicted outcomes or aggregate benefit. The central claim of TACS is that coercion is not a moral failure but a structural incompatibility with stable intelligence operating in shared environments. On this view, alignment is reframed as a problem of constraint enforcement rather than value agreement. The paper presents formal axiom definitions, a reference enforcement architecture, and adversarial analyses illustrating how constraint-based compliance resists common failure modes such as utility tradeoffs, pre-emptive security actions, and social manipulation. By treating autonomy as a hard boundary condition rather than a negotiable preference, TACS offers a model-agnostic framework for non-coercive artificial agency that remains applicable across architectures, cultures, and capability levels.