The article provides a detailed guide on integrating Pydantic AI, a Python agent framework known for its structured data validation and support for multiple LLM providers, with Bright Data's Web MCP server to create AI agents with live data access capabilities. Pydantic AI's unique features include type safety, structured output validation, and optional dependency injection, making it effective for building AI agents capable of complex workflows. By pairing with Bright Data’s Node.js-based MCP server, which provides tools for web scraping and interaction, Pydantic AI agents can overcome limitations such as lack of real-time data access. The guide walks through setting up a Python project, installing necessary libraries, configuring environment variables, and connecting to both the MCP server and a chosen LLM provider, in this case, Google’s Gemini. The integration allows AI agents to perform tasks like real-time data retrieval from web pages, exemplified by successfully extracting product data from Amazon. The article concludes with suggestions for further enhancing the AI agent's capabilities, such as implementing a REPL loop or adding custom tools, and encourages experimentation with Bright Data's comprehensive AI infrastructure.