Agentic Warfare and the Future of Military Operations

The United States’ current military staff system, rooted in a 200-year-old Napoleonic model, is too slow to keep up with modern warfare where AI agents operate in milliseconds. To maintain decision superiority against adversaries such as China, the U.S. Department of Defense must transition to smaller, AI-enabled command structures that are faster and more adaptable. AI now automates intelligence fusion, refines threat assessments, and recommends actions, compressing decision timelines from days to minutes. China’s strategy to disrupt U.S. decision networks through cyber, electronic, and long-range strikes makes traditional, centralized staffs vulnerable.

This report examines three AI-enabled staff models, Networked, Relational, and Adaptive, with the Adaptive model proving most effective and resilient. It stresses the continued importance of human expertise to manage AI systems, verify recommendations, and assume control if networks fail. Smaller, faster staffs generate better options more quickly than larger legacy organizations, but challenges remain, including the need for improved computational infrastructure, cyber-resilient networks, and enhanced AI literacy among officers. Recommendations include sustained experimentation, expanded computing resources, AI-focused education, hardened decision networks, and rapid learning cycles to ensure U.S. forces remain agile and effective in future conflicts.

This report was made possible by support from the Chief Digital and Artificial Intelligence Office (CDAO) with the U.S. Department of Defense. No other direct sponsorship contributed to the research, workshops, and/or the final report.