Foundry IQ: Give Your AI Agents a Knowledge Upgrade
If you’re learning to build AI agents, you’ve probably hit a familiar wall: your agent can generate text, but it doesn’t actually know anything about your data. It can’t look up your documents, search across your files, or pull facts from multiple sources to answer a real question. That’s the gap Foundry IQ fills. It gives your AI agents structured access to knowledge, so they can retrieve, reason over, and synthesize information from real data sources instead of relying on what’s baked into the model. Why Should You Care? As a student or early-career developer, understanding how AI systems work with external knowledge is one of the most valuable skills you can build right now. Retrieval-Augmented Generation (RAG), knowledge bases, and multi-source querying are at the core of every production AI application, from customer support bots to research assistants to enterprise copilots. Foundry IQ gives you a hands-on way to learn these patterns without having to build all the plumbing yourself. You define knowledge bases, connect data sources, and let your agents query them. The concepts you learn here transfer directly to real-world AI engineering roles. What is Foundry IQ? Foundry IQ is a service within Azure AI Foundry that lets you create knowledge bases, collections of connected data sources that your AI agents can query through a single endpoint. Instead of writing custom retrieval logic for every app you build, you: Define knowledge sources — connect documents, data stores, or web content (SharePoint, Azure Blob Storage, Azure AI Search, Fabric OneLake, and more). Organize them into a knowledge base — group multiple sources behind one queryable endpoint. Query from your agent — your AI agent calls the knowledge base to get the context it needs before generating a response. This approach means the knowledge layer is reusable. Build it once, and any agent or app in your project can tap into it. The IQ Series: A Three-Part Learning Path The IQ Series is a set of three weekly episodes that walk you through Foundry IQ from concept to code. Each episode includes a tech talk, visual doodle summaries, and a companion cookbook with sample code you can run yourself. 👉 Get started: https://aka.ms/iq-series Episode 1: Unlocking Knowledge for Your Agents (March 18, 2026) Start here. This episode introduces the core architecture of Foundry IQ and explains how AI agents interact with knowledge. You’ll learn what knowledge bases are, why they matter, and how the key components fit together. What you’ll learn: The difference between model knowledge and retrieved knowledge How Foundry IQ structures the retrieval layer The building blocks: knowledge sources, knowledge bases, and agent queries Episode 2: Building the Data Pipeline with Knowledge Sources (March 25, 2026) This episode goes deeper into knowledge sources, the connectors that bring data into Foundry IQ. You’ll see how different content types flow into the system and how to wire up sources from services you may already be using. What you’ll learn: How to connect sources like Azure Blob Storage, Azure AI Search, SharePoint, Fabric OneLake, and the web How content is ingested and indexed for retrieval Patterns for combining multiple source types Episode 3: Querying Multi-Source Knowledge Bases (April 1, 2026) The final episode shows you how to bring it all together. You’ll learn how agents query across multiple knowledge sources through a single knowledge base endpoint and how to synthesize answers from diverse data. What you’ll learn: How to query a knowledge base from your agent code How retrieval works across multiple connected sources Techniques for synthesizing information to answer complex questions Get Hands-On with the Cookbooks Every episode comes with a companion cookbook in the GitHub repo, complete with sample code you can clone, run, and modify. This is the fastest way to go from watching to building. 👉 Explore the repo: https://aka.ms/iq-series Inside you’ll find: Episode links — watch the tech talks and doodle recaps Cookbooks — step-by-step code samples for each episode Documentation links — official Foundry IQ docs and additional learning resources What to Build Next Once you’ve worked through the series, try applying what you’ve learned: Study assistant — connect your course materials as knowledge sources and build an agent that can answer questions across all your notes and readings. Project documentation bot — index your team’s project docs and READMEs into a knowledge base so everyone can query them naturally. Research synthesizer — connect multiple data sources (papers, web content, datasets) and build an agent that can cross-reference and summarize findings. Start Learning The IQ Series is designed to take you from zero to building knowledge-driven AI agents. Watch the episodes, run the cookbooks, and start experimenting with your own knowledge bases. 👉 https://aka.ms/iq-seriesBuilding ShadowQuest: A Multi-Agent RPG
Artificial Intelligence is rapidly evolving beyond traditional chatbots. Today, developers are building intelligent systems where multiple AI agents collaborate, retrieve knowledge, and solve problems together. Microsoft's Agents League Hackathon provided the perfect opportunity to explore this new approach through the Reasoning Agents challenge. For this challenge, I built ShadowQuest, a fantasy role-playing game (RPG) powered by Microsoft Foundry, Foundry IQ, Azure AI Search, GPT-4.1, and GitHub Copilot. The project demonstrates how specialized AI agents can work together while using Retrieval-Augmented Generation (RAG) to deliver accurate and context-aware responses. About the Challenge Microsoft Agents League is a global developer challenge designed to encourage developers to build intelligent AI applications using Microsoft's latest AI technologies. Participants could choose from three tracks: Creative Apps, Reasoning Agents, and Enterprise Agents. I selected the Reasoning Agents track because I wanted to explore how multiple AI agents could collaborate instead of relying on a single large language model. Another important requirement for this year's challenge was integrating at least one Microsoft Intelligence Layer. For ShadowQuest, I chose Foundry IQ as the project's intelligence layer. The Idea Behind ShadowQuest Fantasy RPGs are built around storytelling, exploration, and collaboration between different characters. Every character usually has a unique role, whether it's a warrior protecting the team, a mage interpreting magical knowledge, or a rogue discovering hidden paths. I wanted to recreate this experience using AI. Instead of building one AI assistant responsible for everything, I designed a system where multiple specialized agents collaborate to create a richer and more immersive adventure. ShadowQuest is set in a fantasy world filled with magical artifacts, forgotten kingdoms, mysterious locations, and story-driven quests. Players can ask questions about the world, explore different locations, and learn about the game's lore through conversations with AI agents. Building the Multi-Agent Architecture The architecture follows a simple but scalable design. At the center of the system is the Game Master Agent, which acts as the orchestrator. Every player interaction starts with the Game Master. It receives the player's request, determines what information is needed, retrieves additional knowledge when required, and generates the final response. Supporting the Game Master are three specialized agents: Warrior Agent – Focuses on combat strategy and tactical decisions. Mage Agent – Provides magical knowledge, world lore, and information about ancient artifacts. Rogue Agent – Specializes in exploration, investigation, and discovering hidden information. Each agent has a clearly defined responsibility, making the system easier to understand, maintain, and extend in the future. Using Foundry IQ as the Knowledge Layer One of the most important parts of the project was integrating Foundry IQ. Instead of storing every piece of game information inside prompts, I created a dedicated knowledge base containing information about characters, magical artifacts, locations, quests, and the history of the ShadowQuest world. This approach separates knowledge from reasoning. Whenever a player asks a question, the Game Master Agent first retrieves relevant information from the knowledge base before generating a response. This ensures that answers remain consistent with the game's world while reducing hallucinations. Foundry IQ became the central source of truth for the entire project, making it easy to manage and expand the game world without constantly modifying prompts. Azure AI Search and Retrieval-Augmented Generation To enable intelligent retrieval, I connected Foundry IQ with Azure AI Search. The RPG documents were indexed, and vector embeddings were generated using Microsoft's embedding models. This enables semantic search, allowing the system to understand the meaning behind a player's question instead of relying only on keyword matching. For example, if a player asks about a magical relic without mentioning its exact name, Azure AI Search can still retrieve the correct information based on semantic similarity. The complete workflow looks like this: The player submits a question. The Game Master Agent receives the request. Foundry IQ queries Azure AI Search. Relevant documents are retrieved. GPT-4.1 generates a grounded response using the retrieved context. This Retrieval-Augmented Generation (RAG) approach significantly improves the quality and reliability of responses. Accelerating Development with GitHub Copilot GitHub Copilot played an important role throughout the development process. It helped generate Python classes, improve documentation, create helper functions, and speed up repetitive coding tasks. During the live demonstration, I also showed how Copilot could quickly generate a new Healer Agent, demonstrating how AI-assisted development makes it easier to extend a multi-agent application while maintaining a consistent architecture. Rather than replacing the developer, Copilot acted as an intelligent coding assistant, allowing me to focus more on architecture and design decisions. Demonstrating ShadowQuest During the Microsoft Agents League Reasoning Agents Battle, I demonstrated the Game Master Agent by asking questions about the ShadowQuest world, magical artifacts, and game lore. One of the most interesting parts of the demonstration was observing the retrieval process. Before generating a response, the Game Master Agent called the knowledge retrieval function through Foundry IQ. This confirmed that the system was retrieving relevant information from the indexed knowledge base rather than relying only on GPT-4.1's internal knowledge. This demonstrated how RAG can create more grounded, reliable, and context-aware AI experiences. Lessons Learned Building ShadowQuest taught me that designing multi-agent systems is as much about architecture as it is about AI models. Clearly defining responsibilities for each agent made the application easier to maintain and opened the door for future expansion. I also learned how valuable Retrieval-Augmented Generation can be for applications that depend on structured knowledge. Separating reasoning from knowledge allows AI systems to remain accurate while making it easier to update information over time. Finally, participating in the Microsoft Agents League was an incredible opportunity to experiment with Microsoft's latest AI technologies, learn from other developers, and share ideas with a global community passionate about agentic AI. Looking Ahead ShadowQuest is only the beginning. In future iterations, I plan to expand the project by introducing additional agents such as a Merchant Agent and Healer Agent, implementing persistent player memory, adding dynamic quest generation, improving combat mechanics, and enabling deeper collaboration between agents. These improvements will make the game world more immersive while continuing to explore the possibilities of agent-based AI systems. Conclusion ShadowQuest demonstrates how Microsoft Foundry, Foundry IQ, Azure AI Search, GPT-4.1, and GitHub Copilot can be combined to build intelligent multi-agent applications. More importantly, the project reinforced an important idea: the future of AI is not a single assistant performing every task, but a team of specialized agents collaborating with shared knowledge to solve increasingly complex problems. Participating in the Microsoft Agents League was an inspiring experience that allowed me to explore the next generation of AI development while building a project that combines storytelling, reasoning, and knowledge retrieval. I look forward to continuing this journey and discovering new ways to build intelligent applications using Microsoft's growing AI ecosystem.
