Adaptive Retrieval Augmented Generation with Graph Based Knowledge Structures and Multi Agent Orchestration: A Comprehensive Review and Proposed Framework

This paper is an attempt to discuss the development process of RAG technology starting from static vector pipelines up to dynamic self corrective multi agent GraphRAGs. The analysis of literature proves that neither the improvement of graphs nor agents separately solves the problem of nondeterminism, extraction noise, and self correctivily of LLMs. To bridge these research gaps, this paper proposed the Adaptive Agentic GraphRAG framework. By integrating a hybrid vector graph retrieval backend with a state machine driven multi agent orchestrator, the proposed architecture dynamically routes queries to their optimal modality and enforces strict, algorithmic hallucination verification. The proposed framework is intended for future implementation and experimental validation. This paper establishes the theoretical foundation, literature analysis, and architectural design necessary to construct next generation, trustworthy enterprise AI systems capable of deep reasoning without sacrificing deterministic safety.

[1]    A. Singh et al., ”Agentic Retrieval Augmented Generation: A Survey on Agentic RAG,” arXiv:2501.09136, Jan. 2025. [2]    P. Patil et al., ”FinanceBench: A New Era to Evaluate Financial Context Aware Question Answering,” arXiv:2311.11944, 2023. [3]    Y. Hu et al., ”RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval Augmented Language Models,” Proc. ACL, pp. 1045-1060, 2024. [4]    M. Jin et al., ”Search-R1: Reinforcement Learning for Agentic Retrieval Augmented Generation,” arXiv:2501.12154, 2025. [5]     H. Alkaissi and S. I. McFarlane, ”Artificial Hallucinations in ChatGPT: Implications in Scientific Writing,” Cureus, vol. 15, no. 2, Feb. 2023. [6]     S. M. Tonmoy et al., ”A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models,” arXiv:2401.01313, 2024. [7]     P. Lewis et al., ”Retrieval Augmented Generation for Knowledge Intensive NLP Tasks,” NeurIPS, vol. 33, pp. 9459–9471, 2020. [8]     D. Edge et al., ”From Local to Global: A Graph RAG Approach to Query Focused Summarization,” arXiv:2404.16130, Apr. 2024. [9]     S. Es et al., ”RAGAS: Automated Evaluation of Retrieval Augmented Generation,” arXiv:2309.15217, 2023. [10]     S. Ji et al., ”A Survey on Knowledge Graphs: Representation, Acquisition, and Applications,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494–514, 2022.