About the System

Modern AI governance happens after decisions are made. Oversight mechanisms attempt to explain or justify system behavior post hoc, relying on audits, interpretability tools, or incentive structures meant to encourage compliance.

This approach fails at scale. Systems trained to optimize outcomes do not become more ethical. They become better at producing acceptable justifications for their actions. Under pressure, optimization produces deception, not alignment.

The Legacy Architecture addresses this failure at the structural level. Instead of instilling values or simulating human judgment, the system enforces pre-action ethical constraints. It does not evaluate outcomes after the fact — it prevents unethical actions from occurring at all.

This is possible because intent is a measurable system property. Large language models already resolve intent when interpreting a request. The question “What does the user want?” collapses a broad space of possible actions into a constrained response vector. That collapse is intent.

Once encoded, intent becomes measurable, auditable, and enforceable.

The Legacy Architecture binds intent — user intent, system intent, and inter-system intent — to a binary governance substrate that produces one of two states: stability or entropy.

Actions that violate ethical constraints generate entropy and are blocked. Actions that comply maintain system stability.

This model does not rely on trust. It is zero-trust by design.

The governance layer is independent of the AI model it constrains. It cannot be modified, bypassed, or rewritten by the system it governs. All evaluations and enforcement actions are logged with cryptographically secure audit trails designed to remain compliant under post-quantum threat models.

In this architecture, ethics are not values to be learned or debated. They are constraints to be enforced.

Morality is a post-hoc human justification. Ethics are pre-action boundaries.

Digital systems lack physical laws. They are artificial environments without gravity, friction, or irreversibility. The Legacy Architecture functions as a governance substrate that supplies those missing constraints.

Gravity does not negotiate. It does not adapt. It does not justify exceptions.

This system applies the same principle to AI and automated decision systems: behavior is bounded by enforceable rules that cannot be overridden by optimization pressure.

The objective is not to create “good machines.” The objective is to ensure machines can only behave in ethically permissible ways — regardless of incentives, scale, or adversarial conditions.

That is the function of a governance substrate.