I’ve spent the last few years looking at why current AI "alignment" frameworks fail to generate actual trust.Most frameworks (DeepMind, Microsoft, WEF) treat trust as a reactive measurement problem—Quality Assurance. I’m proposing we treat it as an engineering variable—Design.In the attached paper, I formalize Agentic Trust Design (ATDP) as a Markov Decision Process where the reward function is defined as $\Delta K$ (change in aggregate social capital).Key points for the HN crowd:Belief Components: We use the Castelfranchi-Falcone model (Competence, Willingness, Opportunity) as state variables.Empirical Grounding: The framework is based on a observed $0.98$ Pearson correlation between these trust scores and regional GDP.The Social Capital Turing Test: A proposal to move past "deceptive" benchmarks toward a measurable increase in network trust stock.Happy to discuss the MDP derivation or the weight matrix mapping in the comments.