International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Autonomous Medallion Orchestration: A Multi-Agent Reinforcement Learning Framework for Financial Ecosystems

Author(s) Uttama Reddy Sanepalli
Country United States
Abstract The rapid migration of financial institutions toward hybrid-cloud lakehouse architectures has introduced unprecedented complexity in balancing data consistency, regulatory compliance, and operational expenditure. Traditional database management relies on reactive administration and static heuristic tuning, which fails to address the dynamic volatility of multi-tenant cloud environments. This research proposes AutoMedallion, an original framework that leverages Multi-Agent Reinforcement Learning (MARL) to facilitate autonomous data governance and resource orchestration. The identified research gap lies in the absence of a closed-loop system that integrates real-time cybersecurity policy enforcement with elastic compute scaling. By employing a dual-agent system, a Performance Agent for SQL optimization and a Governance Agent for automated encryption and PII obfuscation, the framework enables a "self-driving" data tier. Experimental results indicate that AutoMedallion achieves a 40% reduction in cloud compute overhead and a 99.9% adherence rate to SOC2 and GDPR compliance protocols. This study signifies a major advancement in the transition from human-centric database administration to autonomous, resilient, and policy-aware data ecosystems within the mission-critical financial sector.
Keywords Multi-Agent Reinforcement Learning, Medallion Architecture, Cloud Governance, Autonomous Databases, Hybrid Lakehouse, Financial Data Systems, Cost Optimization.
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-07
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.69007
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E-ISSN 2582-2160
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