Enterprise AI agents need more than prompts and model access. They need governed data, carefully designed tools, secure identity, reliable orchestration, validation, and production monitoring. In this session, attendees will see how to build an enterprise data agent using Azure Databricks, Microsoft Agent Framework, Azure AI Foundry, and Model Context Protocol. The demonstration follows a financial variance-analysis use case in which an agent retrieves authorized data, calls governed tools, validates structured responses, and escalates sensitive actions for approval. The session also covers MCP security boundaries, prompt-injection defenses, evaluation, tracing, and deployment patterns that developers can apply to production agent solutions.