Methodologies for Designing Enterprise-Grade Data Pipelines for AI Agents in Regulated Industries

The study examines engineering methodologies for building enterprise-grade data pipelines that support AI agents under stringent regulatory constraints in finance, insurance, healthcare, and payroll domains. The research addresses the fragmentation of multi-region data flows, the heterogeneity of legacy ERP environments, and the growing load from AI workloads that depend on reproducible, ACID-compliant lakehouse architectures and feature-store-centric design. The work generalizes recent advances in Delta Lake–based reliability patterns, AI/ML-optimized lakehouses, feature stores, and AI-driven compliance automation, integrating them into a unified blueprint for multi-tenant, audit-ready pipelines. The goal of the article is to synthesize a reliability framework for financial data, a multi-tenant AI lakehouse model for payroll, and a novel multi-file validation and reconciliation pattern for high-risk financial ETL. Comparative analysis, source criticism, and architectural synthesis are applied to a curated set of recent scientific and professional publications. The conclusions describe how these patterns reduce reconciliation effort, strengthen regulatory assurance, and create AI-ready data foundations. The article targets data engineers, architects, and technical leaders who design AI-enabled systems in regulated industries.