Beyond Human-in-the-Loop: A Level 5 Autonomous Multi-Agent Framework for Master Data Management and Governance using Graph States and Epistemic Uncertainty Estimation

Abstract

The enterprise data landscape is navigating a critical inflection point where the exponential growth of unstructured data has rendered traditional Data Stewardship models unsustainable. While the industry has historically relied on Rule-Based Systems and Human-Guided Machine Learning, these approaches fail to scale linearly with data volume, precipitating a stewardship crisis defined by high latency and prohibitive costs. This paper presents an exhaustive analysis of a novel Level 5 Autonomous Data Fabric that fundamentally reimagines Master Data Management and Governance. By leveraging a Graph-Based Multi-Agent System rooted in a Neo4j graph state, this framework utilizes Generative AI Agents to perform semantic data cleaning and Entity Resolution. Unlike linear data pipelines that move data through a fragile series of transformations, this system treats data as a mutating state, employing Worker and Critic agent pods to ensure accuracy while mitigating hallucination risks through Epistemic Uncertainty Estimation. Key findings from a controlled simulation of 100,000 records indicate that this agentic architecture achieves a 99.9% cost reduction and a 750x throughput improvement compared to human baselines, while maintaining a Pairwise F1- Score of 0.968. This research validates that the transition from Human-in-the-Loop to Human- Exception governance is not only technically feasible but economically imperative for modern enterprises.