Agentic AI for Regulatory Intelligence: Designing Scalable Compliance Lifecycle Systems in Multinational Tech Enterprises

Abstract

The rapid expansion of multinational technology enterprises, particularly in highly regulated sectors such as e-commerce and healthcare, has amplified the complexity of managing diverse and evolving global compliance requirements. Traditional regulatory monitoring and response models—largely manual and reactive—are no longer scalable in the face of dynamic legislation, cross-border data governance rules, and sector-specific standards. This paper proposes an agentic AI–driven regulatory intelligence framework designed to automate and optimize the entire compliance lifecycle across jurisdictions. Leveraging lessons from Amazon’s large-scale operational structure, the study explores the integration of horizon scanning, natural language understanding, autonomous policy interpretation, and AI-driven risk assessment to enable real-time detection of regulatory changes, automated control mapping, and proactive remediation workflows. The system architecture includes distributed AI agents capable of orchestrating governance tasks across departments while maintaining auditability, human oversight, and ethical alignment. By transitioning compliance from a static, document tation-heavy function to a dynamic, intelligence-led ecosystem, this research demonstrates how agentic AI can significantly reduce regulatory exposure, enhance operational resilience, and enable strategic decision-making at global scale. The proposed model offers a blueprint for enterprises seeking to future-proof their compliance operations amidst increasing regulatory volatility.