Autonomous AI Agents for Dynamic Web Navigation: Design and Implementation of a Vendor Credentialing Verification System

Vendor credentialing and license verification in regulated industries require accurate, current records distributed across more than fifty independent state licensing systems in the United States, each with distinct interface designs, navigation patterns, and data formats. Manual verification workflows cannot maintain data freshness at enterprise scale, while conventional static scraping tools fail silently when portal interfaces change. This paper presents the design and implementation of an autonomous AI agent framework for dynamic web navigation and distributed license verification, deployed in production at RealPage to support contractor credentialing for residential and commercial real estate maintenance services. The system integrates state machine-driven agent orchestration, vision-based interface understanding via large multimodal models with Set-of-Mark visual grounding, a dynamic URL discovery and validation mechanism, and a distributed multi-state verification infrastructure enabling concurrent execution across all fifty U.S. states. Deployment data indicates a reduction of 70 to 85 percent in manual verification effort, a 60 percent improvement in credential data freshness, end-to-end verification times averaging three to six seconds per request, and data extraction accuracy between 92 and 96 percent across supported states and contractor sectors. The contribution of this work is a production-validated reference architecture for enterprise-grade autonomous compliance verification that generalizes to any domain requiring distributed, adaptive verification across heterogeneous regulatory systems.