A Framework for Embedding Generative and Agentic AI in Open Source Intelligence

Open Source Intelligence (OSINT) plays a critical role in cybersecurity and threat intelligence. However, traditional methods are slow, manual, and difficult to scale. Although Large Language Models (LLMs) and Generative AI have been explored for OSINT, most existing approaches apply them to isolated tasks without developing an integrated and autonomous architecture. This paper proposes a novel Agentic AI-driven OSINT framework that enables autonomous information gathering, reasoning, and tool orchestration across heterogeneous open-source data streams. The system uses retrieval-augmented generation (RAG), chain-of-thought reasoning, and adaptive agent planning to determine which tools to invoke, how to process intermediate outputs, and when to escalate findings for human review. The proposed architecture includes modules for multi-source data ingestion, LLM-powered analysis, generative scenario simulation, and ethical safeguard enforcement. A proof-of-concept use case involving the location of missing persons from public data demonstrates how the framework improves coverage, accuracy, and decision speed when compared to conventional OSINT workflows. This work introduces the first unified and reproducible design for an Agentic AI OSINT system that incorporates transparency, accountability, and ethical compliance into its operational core.