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…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.