Employing AI Agents for Designed Data Architectures into Data Mesh Using Data Metrics to Enable Efficient Management Decisions in Financial Sector

Abstract

This research explores the integration of AI agents into innovative data architecture models, focusing on their application within decentralized frameworks like Data Mesh. By leveraging data metrics as foundational elements, the proposed bottom-up model aligns business objectives with technical infrastructure, addressing challenges in data quality and governance. AI agents enhance this implementation by automating processes such as metadata tagging, schema evolution, and optimization modeling. The conceptual multi-agent flow model demonstrates how specialized agents collaborate across layers to ensure scalability, compliance, and actionable insights. Early trials in the financial sector validate the model’s potential to improve efficiency and decision-making.