Architecting an Autonomous Compliance Infrastructure: An Agentic AI Framework for Legal-Critical Tax Systems

Abstract

Enterprise software infrastructure operates under stringent regulatory, security, and operational governance constraints. The manual translation of natural-language statutes into executable application logic introduces measurable error rates and systemic latency, with legacy update cycles requiring three to sixteen months for full integration. This article proposes a formalized Autonomous Compliance Infrastructure (ACI) powered by an Agentic AI framework capable of continuously ingesting, translating, and deploying regulatory updates into legal-critical enterprise tax systems. The architecture enforces a Tax-to-Code separation model, decoupling declarative regulatory policy from core business logic, and integrates a Human-in-the-Loop governance model via Explainable AI for uncertainty quantification. Modeled evaluations demonstrate a reduction in compliance update lifecycle from months to hours, with an end-to-end update cycle time compressing from three to sixteen months to twelve to forty-eight hours. The framework addresses the research question of whether agentic AI architectures can safely operationalize continuous regulatory compliance in legal-critical enterprise systems.