Year: 2026 | Month: February | Volume: 13 | Issue: 2 | Pages: 357-380
DOI: https://doi.org/10.52403/ijrr.20260235
Framework for Federal AI Adoption: Governance, Standards, and Implementation Pathways for Agentic Gen AI
Satyadhar Joshi1
1Alumnus MSIT Touro College, NYC
1Alumunus IMBA Bar Ilan University, Israel
Corresponding Author: Satyadhar Joshi
ABSTRACT
The United States federal government stands at a critical inflection point in artificial intelligence adoption, transitioning from experimental deployments to integration of autonomous systems across all agencies. This comprehensive paper presents a multi-layered national governance framework addressing the unique challenges posed by agentic AI—systems capable of autonomous decision-making, planning, and tool interaction without continuous human supervision. Building upon Executive Order 14110, OMB Memorandum M-25-21, NIST AI Risk Management Framework, and international standards including ISO/IEC 42001, we conduct an exhaustive analysis of regulatory barriers, technical interoperability challenges, and organizational readiness gaps. Through systematic examination of government publications, industry whitepapers, academic research, and regulatory filings, we identify and categorize five primary barriers to federal AI adoption: regulatory mismatches, structural incompatibility, lack of regulatory clarity, direct hindrance mechanisms, and organizational factors. Our proposed framework integrates three pillars: (1) Technical standards harmonization incorporating ISO/IEC 42001, NIST AI RMF, IEEE 7000, HL7 FHIR, and emerging agentic AI protocols; (2) A four-tier risk-based classification system aligned with the EU AI Act and tailored for U.S. federal requirements; (3) Administrative flexibility mechanisms including regulatory sandboxes, conditional approvals, and graduated compliance enforcement. We provide detailed implementation roadmaps across four phases (immediate, short-term, medium-term, and long-term), sector-specific pathways for healthcare, financial services, transportation, defense, and federal operations, and numerous TikZ diagrams for policy visualization. Key contributions include: (a) A comprehensive synthesis of agentic AI governance requirements for federal adoption; (b) A comparative analysis of international AI governance frameworks; (c) Technical specifications for AI interoperability across federal domains; (d) Workforce development frameworks addressing multiple competency areas; (e) Economic impact projections estimating significant annual productivity gains through responsible AI adoption. This framework provides policymakers, agency leaders, and industry stakeholders with actionable pathways to accelerate responsible AI innovation while maintaining public trust, safety, and U.S. global competitiveness in the agentic AI era.
Keywords: Artificial Intelligence, Agentic AI, Federal Policy, AI Governance, Risk Management, NIST AI RMF, ISO/IEC 42001, Regulatory Reform, Interoperability, Multi-Agent Systems, Autonomous Systems, AI Ethics, Compliance Framework, Digital Government, AI Standards, Workforce Development, Critical Infrastructure, National Security
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