Agentic AI for National Defense: Review of Architectural Patterns and Recent Developments

Satyadhar Joshi

Abstract: This paper presents a systematic review of Agentic Artificial Intelligence (AI) systems and their emerging role in military applications. Agentic AI represents a fundamental paradigm shift from reactive, task-specific AI to autonomous, goal-oriented agents capable of independent reasoning, decision-making, and action execution in dynamic environments. We analyze the architectural frameworks enabling Agentic AI deployment across key military domains, including hierarchical command-and-control systems, multi-agent cyber defense networks, and human-AI collaborative interfaces. The review synthesizes recent advancements in machine learning models-such as transformer-based architectures, mixture-of-experts (MoE) systems, and reinforcement learning agents-with quantified performance metrics including sub-100ms response times, 99.9% accuracy in battlefield management, and throughput rates exceeding 2.5 million events per second in cyber operations. Technical challenges such as latency constraints, model scalability (up to 1.76 trillion parameters), energy consumption (1-10 kW per system), and integration with legacy military infrastructure are critically examined. Ethical and security considerations, including explainable AI (XAI) compliance, adversarial robustness, and human oversight mechanisms, are evaluated alongside operational implementations.

External IDs:doi:10.36227/techrxiv.176045912.25605018/v1