Data Stewardship: How AI Agents Form the Pillars for Effective Data and AI Governance

Abstract

The exponential growth of enterprise data and the rapid adoption of artificial intelligence (AI) have fundamentally altered the landscape of data governance. Traditional, manual approaches to data stewardship are increasingly inadequate for managing the complexity, velocity, and scale of modern data ecosystems. In response, agentic AIautonomous systems capable of interpreting intent, executing workflows, and adapting to dynamic conditions has emerged as a transformative solution. This manuscript provides a comprehensive analysis of how AI agents are redefining data stewardship, establishing the foundational pillars for effective data and AI governance. It examines the deployment of specialized AI agents across critical governance domains, including data quality management, metadata curation, master data management, and data retention. Furthermore, this paper addresses the imperative of regulatory compliance and responsible AI frameworks, demonstrating how multi-agent architectures enable real-time policy enforcement and risk mitigation. Through a synthesis of recent technological advancements and enterprise implementation models, this study underscores that agentic data governance is not merely an operational enhancement, but a critical evolution necessary for organizations to maintain data integrity, security, and trust in the AI era.