A Proposal for an Agentic AI Architecture to Support Multi-Domain Decision-Making in the Brazilian Armed Forces

📅 2026-09-17
📈 Citations: 0
Influential: 0
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🤖 AI Summary
针对多域决策环境的复杂性,提出一种自主代理AI架构,以支持巴西武装部队的决策,涵盖数据访问、执行工具及安全保障。
📝 Abstract
The growing complexity of multi-domain operational environments (land, aerospace, naval, cyber, and electromagnetic spectrum) has increased the volume and velocity of data reaching command-and-control (C2) centers, straining the observe-orient-decide-act (OODA) decision cycle. Artificial Intelligence (AI) systems currently employed in defense are, in general, reactive and isolated tools that still rely heavily on human operators to integrate information, assess scenarios, and formulate courses of action. This paper proposes a conceptual Agentic AI architecture for AI systems that can plan, access data sources, execute tools, and act autonomously and audibly, aimed at supporting decision-making across the three Brazilian Armed Forces (Navy, Army, and Air Force). Four application fronts are discussed (decision support, situational analysis, feasibility studies, and countermeasure suggestion), as well as the data and sensor access requirements and the security and permission safeguards necessary for responsible employment across administrative, strategic, operational, and tactical contexts.
Problem

Research questions and friction points this paper is trying to address.

Multi-Domain Operational Environments
Command-and-Control Centers
OODA Decision Cycle
AI Systems
Innovation

Methods, ideas, or system contributions that make the work stand out.

Agentic AI
multi-domain decision-making
autonomous action
decision support
situational analysis
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