Building 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.
