Introduction

Your organization is adopting AI agents to automate tasks and boost productivity. But you discover a fundamental challenge: how do you scale agents beyond simple, predefined tasks?

The real power of AI agents emerges when they can access your organization's knowledge. This knowledge includes your policies, procedures, product documentation, support articles, and domain expertise built over years.

Traditional AI agents have significant limitations. They can't access your private data, they're constrained by knowledge cutoff dates, and they generate generic responses without your company's context. When they lack factual grounding, they often create incorrect information.

If you want to build knowledge-enabled agents, you face complex engineering challenges. You need to connect to data sources, implement chunking strategies, build vector databases, and manage access controls. Each team tackles these same problems repeatedly.

Foundry IQ is Microsoft's unified knowledge platform that transforms how your AI agents access organizational data. Instead of rebuilding custom Retrieval Augmented Generation (RAG) pipelines for every project, you get a shared knowledge management system. Multiple agents can access the same knowledge bases, and improvements to those knowledge bases benefit every connected agent immediately.

Learning objectives

In this module, you learn to:

• Explain how RAG solves the knowledge problem by connecting agents to real-time information
• Describe how Foundry IQ provides a shared knowledge platform that multiple agents can access
• Configure data sources for knowledge bases including Azure AI Search, Blob Storage, SharePoint, and OneLake
• Configure agent instructions to control retrieval behavior and ensure consistent citations
• Test and monitor agent retrieval to maintain quality in production

Let's start by discovering how Retrieval Augmented Generation (RAG) transforms simple agents into powerful knowledge-enhanced assistants.