Large Language Models (LLMs) possess remarkable generative capabilities but are fundamentally constrained by their static, pre-trained knowledge. This paper introducesa novel theoretical architectural framework for an agentic LLM system designed for self-directed knowledge acquisition. The proposed system aims to autonomously identify its knowledge gaps, explore external information sources such as the World Wide Web, rigorously validate acquired data, and integrate new, verified knowledge into an accessible, modifiable external repository. Crucially, this conceptual proposal is designed to operate without direct human intervention in the core acquisition loop and, critically, without altering the LLM’s underlying parametric weights. The framework delineates seven key conceptual components: a “Curiosity Service” for identifying knowledge lacunae, a “Subconscious Mind” for temporary concept storage, an “Agentic Web Exploration” module for information retrieval, an “Ingestion and Processing” unit for data extraction, a multi-stage “Validation Pipeline” for ensuring data integrity, a “Data Admissibility Rules” engine for filtering, and a “Long-Term Memory” based on Graph-Retrieval Augmented Generation (Graph-RAG) for persistent knowledge integration. This paper details the proposed architecture, its theoretical underpinnings, conceptual operational dynamics, and potential challenges, positioning it as a visionary roadmap for future research in continuously learning AI systems. While foundational aspects of certain components are considered prototypable with current technologies, full empirical validation of the integrated, autonomous system represents a significant undertaking beyond the scope of typical independent research resource limitations.