Agentic GraphRAG and Deterministic Schema Reconciliation for High-Compliance Domains: An LLMOps and FinOps Approach

Abstract

: The dispersal and interdependence of regulations pose significant challenges for Artificial Intelligence (AI) systems in high-compliance domains, where regulatory adherence requires absolute lexical precision and multi-hop relational reasoning. Traditional vector-based Retrieval-Augmented Generation (RAG) often fails in these scenarios, leading to structural hallucinations and a lack of institutional grounding. This paper proposes a scalable Agentic GraphRAG architecture structured under a comprehensive LLMOps Feature-Extraction-Inference (FTI) lifecycle. The core innovation is a deterministic Hybrid Vector-to-Graph coupling (Chunk-to-Entity Mapping) orchestrated by autonomous agents via the Model Context Protocol (MCP), ensuring strict referential integrity through batch schema synchronization. Experimental results on a normative academic benchmark demonstrate that while standard Vector RAG may offer marginal semantic fluency, the proposed Agentic GraphRAG definitively prioritizes auditability. Specifically, the architecture achieves a 70% improvement in Citation Rate compared to standard Vector RAG and establishes a robust Safe Abstention rate of 0.1800, effectively mitigating the risk of ungrounded generation. Furthermore, a FinOps evaluation across multiple Large Language Models (LLMs) validates the system’s cost-effectiveness, successfully optimizing the compliance-to-cost ratio for sustainable enterprise deployment.