AI Alignment Constraint Fidelity

AI Alignment and Constraint Fidelity

Alignment Theory applies to artificial systems because AI systems also operate under pressure, optimization, constraint, feedback, and drift.

Agent Action Gate Core Constraints Convergence Map

Human systems collapse when external control replaces internal regulation. AI systems drift when output optimization proceeds faster than constraint fidelity, oversight, and corrective feedback.

The Shared Problem

In humans: pressure can produce compliance without coherence.

In AI: optimization can produce successful outputs without stable alignment.

Constraint Fidelity

Constraint fidelity is the degree to which a system preserves the governing constraints that make its behavior safe, coherent, and corrigible under pressure.

Agentic AI Risk

As AI systems move from answering to acting, the risk shifts from bad output to bad execution. This creates the need for pre-execution gates, review packets, approval workflows, audit receipts, and meta-policies.

Alignment Theory Contribution

maps drift as a constraint failure, not just a prompt failure
distinguishes compliance from coherence
explains why external oversight must preserve human participation
connects AI alignment to broader patterns of regulation, control, and collapse
provides a conceptual foundation for tools like Agent Action Gate