Towards Explainable AI in Agentic Retrieval-Augmented Generation: A Systematic Review

Abstract

The development of large language models (LLMs) is rapidly advancing, with the recent Retrieval Augmented Generation (RAG) systems and Agentic RAG systems, which combine external knowledge to improve factual accuracy and extend the autonomous reasoning processes, multi-step planning, and tool interaction to address complex tasks. The complex structure of retrieval, planning, and generation processes that the composition of the Agentic RAG systems poses considerable difficulty in matters of transparency, accountability, and trust to the user. The field of Explainable AI (XAI) in Agentic RAG is relatively unexplored, with different approaches and an absence of agreed-upon methods of evaluation. This survey provides a depth, systematic overview of the XAI methods suitable for the advanced Agentic RAG techniques, along with component-specific approaches for retrievers, planners/agents, and generators, as well as end-to-end pipeline-level explanations. Followed PRIMSA and PICO (Population, Intervention, Comparison, Outcome) guidelines, and for this review, collected data from different databases like IEEE Xplore (61 articles), PubMed (31 articles), and other databases (25 articles) from 2017 to 2025. The Rayyan AI tool is used in the process to remove duplicates and screen the articles. Initially reviewed articles based on title and abstract, followed by full text. To assess the quality of selected articles Meta Quality Appraisal Tool (MetaQAT) was used, and after screening, 39 records were excluded. In the full-text review of 78 articles, 34 articles were lacking AI relevance, thus excluded. Ultimately, 44 key articles were identified for their contribution to LLM, Agentic RAG, and Explainable AI. This review provides a comprehensive analysis of Explainable AI (XAI) techniques in Agentic Retrieval-Augmented Generation (Agentic RAG), guiding future research and advancing the fields of explainable AI (XAI), large language models (LLMs), and Agents. © 2025 IEEE.