1. Challenge: Brittle Web Scrapers at Enterprise Scale
Extracting product catalogs, pricing intelligence, and market data across hundreds of dynamic third-party web portals has historically required fragile XPath/CSS selector scripts that break whenever a target website updates its DOM hierarchy or triggers CAPTCHA challenges.
To solve this, I developed an autonomous agentic browser automation solution combining Browser-Use, LangChain, and Azure AI Foundry. The agent visually observes web pages, understands interactive UI elements, plans multi-step navigation paths, and extracts structured intelligence without depending on brittle hardcoded selectors.
Autonomous Navigation Pipeline
Azure AI FoundryCaptures high-resolution viewport screenshots and interactive accessibility trees, mapping bounding boxes to clickable buttons, search fields, and pagination controls.
LangChain reasoning agent decides whether to type search terms, click filters, scroll to trigger lazy loading, or handle cookie modals.
Converts raw web content into strongly typed JSON records, validating SKU numbers, prices, specifications, and availability.
Runs parallel headless browser containers in Azure, streaming normalized datasets into enterprise analytical data warehouses.
Business Impact & Efficiency Gains
- ✓80%+ reduction in manual data extraction and catalog curation time.
- ✓95%+ scraper uptime despite frequent upstream third-party UI redesigns.
- ✓Seamless enterprise integration with Azure AI Foundry for monitoring and tracing.