Agentic Retrieval-Augmented Generation (RAG): An Adaptive Framework for Evidence-Grounded Question Answering

This paper presents a proposed Agentic Retrieval-Augmented Generation (RAG) framework for adaptive, evidence-grounded question answering using large language models (LLMs). Unlike conventional single-pass RAG systems, the proposed framework incorporates query planning, retrieval, tool selection, evidence evaluation, answer generation, and self-reflection. The framework uses a bounded feedback loop to improve evidence retrieval when the initial results are insufficient or conflicting. It aims to produce answers supported by relevant source citations while communicating uncertainty when reliable evidence is unavailable. The paper discusses the system architecture, workflow, algorithmic control logic, evaluation methodology, security, privacy, and governance considerations. It also proposes evaluation metrics for answer quality, retrieval effectiveness, factual grounding, latency, computational cost, and component-level ablation studies. Potential applications include research assistance, document question answering, enterprise knowledge management, customer support, and data analysis. This work presents a conceptual framework and proposed evaluation plan. Implementation, benchmark experiments, and measured performance improvements are not reported. Keywords: Agentic RAG, Retrieval-Augmented Generation, Large Language Models, AI Agents, Evidence-Grounded Question Answering, Adaptive Retrieval, Self-Reflection.