mlsanityData to 14 Oct 2026

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

arXiv 2609.20080 ↗First posted 17 Sep 2026cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Agentic AI for wireless networks also fits: General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 13 of its 19 references that are in the map. First posted on arXiv 17 Sep 2026.

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13 of 19 references.

Closest ancestors · 1

Same direction, newest first

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Other references · 12

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  7. 2026The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Breakcited by 25
  8. 2026MalTool: Malicious Tool Attacks on LLM Agentscited by 20
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  10. 2025Preventing Prompt Injection with Type-Directed Privilege Separationcited by 9
  11. 2025Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligencecited by 6
  12. 2026AgentWatcher: A Rule-based Prompt Injection Monitorcited by 6

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