The deployment of Large Language Models (LLMs) in sensitive, disconnected environments such as air-gapped military networks introduces challenges related to knowledge staleness, strict data isolation, security assurance, and adaptability. While Retrieval-Augmented Generation (RAG) improves factual grounding by integrating local knowledge sources, conventional RAG pipelines remain static and lack autonomous adaptability for complex intelligence tasks. Agentic RAG extends this paradigm through autonomous agents but typically assumes online connectivity and external feedback, rendering it unsuitable for classified, offline deployments. Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. This paper proposes a novel conceptual framework, the Autonomous Agentic RAG Loop (AARL), which integrates multi-agent coordination, adaptive retrieval, and secure reasoning for offline intelligence systems. The AARL architecture introduces agents with specific cognitive roles (Retriever, Generator, Evaluator, and Orchestrator) operating within isolated computation modules called Distributed Cognitive Cells (DCCs). The framework features a self-correcting feedback loop driven by a Self-Adaptive Reinforcement Mechanism (SARM) that enables continuous improvement without external dependencies. This paper provides both theoretical analysis of the AARL\\\'s expected properties and a detailed feasibility assessment, demonstrating that its design offers a significant step toward building self-sufficient, secure, and trustworthy AI systems for defence and critical infrastructure applications.
Gupta et al. (2026) studied this question.