Securing U.S. Leadership in Agentic AI Literacy and Adoption: U.S. vs Chinese Government Policies and Initiatives

This paper conducts a rigorous comparative analysis of U.S. and Chinese strategic frameworks for AI literacy and adoption, with specialized focus on agentic AI systems capable of autonomous reasoning and execution. We systematically examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent strategic paradigms: the U.S. favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; China pursues centralized, state-led strategies with comprehensive content labeling and rapid systemic integration. We propose a hybrid governance architecture that synthesizes strengths from both models, supported by algorithmic implementations and sensitivity analyses. Drawing from recent publications (2021-2025), we identify critical trends, challenges, and strategic implications. The paper concludes with evidence-based recommendations for policymakers, educators, and industry stakeholders navigating the complex landscape of global AI competition. The paper concludes with actionable recommendations for policymakers, educators, and industry leaders engaged in the global AI race.