Agentic Generative AI and National Security: Policy Recommendations for US Military Competitiveness

This paper presents a comprehensive analysis of Agentic Gen Artificial Intelligence (AI) frameworks and their integration into modern military systems. We examine the architectural foundations, development pipelines, and security considerations for deploying autonomous AI agents in defense applications. The research analyzes multi-agent system architectures, digital twin environments for training and validation, and secure DevOps pipelines tailored for military AI deployment. Through detailed technical diagrams and case studies, we demonstrate how Agentic AI systems enable proactive decision-making, adaptive mission planning, and coordinated autonomous operations across domains including command and control, intelligence surveillance reconnaissance (ISR), cyber defense, and swarm warfare. The paper identifies critical technical challenges in system integration, adversarial robustness, and human-machine teaming, while proposing layered security frameworks and standardized interoperability protocols. Our findings indicate that successful military implementation of Agentic AI requires robust testing methodologies, explainable AI components, and ethical governance mechanisms to ensure reliability, accountability, and compliance with international norms. The technical analysis provides a foundation for future research on AGI integration and offers practical recommendations for defense organizations navigating the transition to agentic warfare systems.