Level up your Python + AI skills with our complete series
We've just wrapped up our live series on Python + AI, a comprehensive nine-part journey diving deep into how to use generative AI models from Python. The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To help you apply what you've learned, all of our code examples work with GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. If you're a Spanish speaker, check out the Spanish version of the series. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across many types of content. In this session, we explore different vector embedding models, such as the OpenAI text-embedding-3 series, through both visualizations and Python code. We compare distance metrics, use quantization to reduce vector size, and experiment with multimodal embedding models. Slides for this session Code repository with examples: vector-embedding-demos Python + AI: Retrieval Augmented Generation 📺 Watch recording In our third session, we explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that provides context to the LLM, enabling it to deliver well-grounded answers for a particular domain. The RAG approach works with many types of data sources, including CSVs, webpages, documents, and databases. In this session, we walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vision models 📺 Watch recording Our fourth session is all about vision models! Vision models are LLMs that can accept both text and images, such as GPT-4o and GPT-4o mini. You can use these models for image captioning, data extraction, question answering, classification, and more! We use Python to send images to vision models, build a basic chat-with-images app, and create a multimodal search engine. Slides for this session Code repository with examples: openai-chat-vision-quickstart Python + AI: Structured outputs 📺 Watch recording In our fifth session, we discover how to get LLMs to output structured responses that adhere to a schema. In Python, all you need to do is define a Pydantic BaseModel to get validated output that perfectly meets your needs. We focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples demonstrate the many ways you can use structured responses, such as entity extraction, classification, and agentic workflows. Slides for this session Code repository with examples: python-openai-demos Python + AI: Quality and safety 📺 Watch recording This session covers a crucial topic: how to use AI safely and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. We focus on Azure tools that make it easier to deploy safe AI systems into production. We demonstrate how to configure the Azure AI Content Safety system when working with Azure AI models and how to handle errors in Python code. Then we use the Azure AI Evaluation SDK to evaluate the safety and quality of output from your LLM. Slides for this session Code repository with examples: ai-quality-safety-demos Python + AI: Tool calling 📺 Watch recording In the final part of the series, we focus on the technologies needed to build AI agents, starting with the foundation: tool calling (also known as function calling). We define tool call specifications using both JSON schema and Python function definitions, then send these definitions to the LLM. We demonstrate how to properly handle tool call responses from LLMs, enable parallel tool calling, and iterate over multiple tool calls. Understanding tool calling is absolutely essential before diving into agents, so don't skip over this foundational session. Slides for this session Code repository with examples: python-openai-demos Python + AI: Agents 📺 Watch recording In the penultimate session, we build AI agents! We use Python AI agent frameworks such as the new agent-framework from Microsoft and the popular LangGraph framework. Our agents start simple and then increase in complexity, demonstrating different architectures such as multiple tools, supervisor patterns, graphs, and human-in-the-loop workflows. Slides for this session Code repository with examples: python-ai-agent-frameworks-demos Python + AI: Model Context Protocol 📺 Watch recording In the final session, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible. We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like LangGraph and Microsoft agent-framework to MCP servers. With great power comes great responsibility, so we briefly discuss the security risks that come with MCP, both as a user and as a developer. Slides for this session Code repository with examples: python-mcp-demoMicrosoft AI Agents Learn Live Starting 15th April
Join us for an exciting Learn Live webinar where we dive into the fundamentals of using Azure AI Foundry and AI Agents. The series is to help you build powerful Agent applications. This learn live series will help you understand the AI agents, including when to use them and how to build them, using Azure AI Agent Service and Semantic Kernel Agent Framework. By the end of this learning series, you will have the skills needed to develop AI agents on Azure. This sessions will introduce you to AI agents, the next frontier in intelligent applications and explore how they can be developed and deployed on Microsoft Azure. Through this webinar, you'll gain essential skills to begin creating agents with the Azure AI Agent Service. We'll also discuss how to take your agents to the next level by integrating custom tools, allowing you to extend their capabilities beyond built-in functionalities to better meet your specific needs. Don't miss this opportunity to gain hands-on knowledge and insights from experts in the field. Register now and start your journey into building intelligent agents on Azure Register NOW Learn Live: Master the Skills to Create AI Agents | Microsoft Reactor Plan and Prepare to Develop AI Solution on Azure Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team. Learning objectives By the end of this module, you'll be able to: Identify common AI capabilities that you can implement in applications Describe Azure AI Services and considerations for using them Describe Azure AI Foundry and considerations for using it Identify appropriate developer tools and SDKs for an AI project Describe considerations for responsible AI Format: Livestream Topic: Core AI Language: English Details Fundamentals of AI agents on Azure AI agents represent the next generation of intelligent applications. Learn how they can be developed and used on Microsoft Azure. Learning objectives By the end of this module, you'll be able to: Describe core concepts related to AI agents Describe options for agent development Create and test an agent in the Azure AI Foundry portal Format: Livestream Topic: Core AI Language: English Details Develop an AI agent with Azure AI Agent Service This module provides engineers with the skills to begin building agents with Azure AI Agent Service. Learning objectives By the end of this module, you'll be able to: Describe the purpose of AI agents Explain the key features of Azure AI Agent Service Build an agent using the Azure AI Agent Service Integrate an agent in the Azure AI Agent Service into your own application Format: Livestream Topic: Core AI Language: English Details Integrate custom tools into your agent Built-in tools are useful, but they may not meet all your needs. In this module, learn how to extend the capabilities of your agent by integrating custom tools for your agent to use. Learning objectives By the end of this module, you'll be able to: Describe the benefits of using custom tools with your agent. Explore the different options for custom tools. Build an agent that integrates custom tools using the Azure AI Agent Service. Format: Livestream Topic: Core AI Language: English Details Develop an AI agent with Semantic Kernel - Training | Microsoft Learn By the end of this module, you'll be able to: Use Semantic Kernel to connect to an Azure AI Foundry project Create Azure AI Agent Service agents using the Semantic Kernel SDK Integrate plugin functions with your AI agent Develop an AI agent with Semantic Kernel Format: Livestream Topic: Core AI Language: English Details Details Orchestrate a multi-agent solution using Semantic Kernel Learn how to use the Semantic Kernel SDK to develop your own AI agents that can collaborate for a multi-agent solution. Learning objectives By the end of this module, you'll be able to: Build AI agents using the Semantic Kernel SDK Develop multi-agent solutions Create custom selection and termination strategies for agent collaboration Format: Livestream Topic: Core AI Language: English DetailsIntegrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!Multi-Agent Systems and MCP Tools Integration with Azure AI Foundry
The Power of Connected Agents: Building Multi-Agent Systems Imagine trying to build an AI system that can handle complex workflows like managing support tickets, analyzing data from multiple sources, or providing comprehensive recommendations. Sounds challenging, right? That's where multi-agent systems come in! The Develop a multi-agent solution with Azure AI Foundry Agent Services module introduces you to the concept of connected agents a game changing approach that allows you to break down complex tasks into specialized roles handled by different AI agents. Why Connected Agents Matter As a student developer, you might wonder why you'd need multiple agents when a single agent can handle many tasks. Here's why this approach is transformative: 1. Simplified Complexity: Instead of building one massive agent that does everything (and becomes difficult to maintain), you can create smaller, specialized agents with clearly defined responsibilities. 2. No Custom Orchestration Required: The main agent naturally delegates tasks using natural language - no need to write complex routing logic or orchestration code. 3. Better Reliability and Debugging: When something goes wrong, it's much easier to identify which specific agent is causing issues rather than debugging a monolithic system. 4. Flexibility and Extensibility: Need to add a new capability? Just create a new connected agent without modifying your main agent or other parts of the system. How Multi-Agent Systems Work The architecture is surprisingly straightforward: 1. A main agent acts as the orchestrator, interpreting user requests and delegating tasks 2. Connected sub-agents perform specialized functions like data retrieval, analysis, or summarization 3. Results flow back to the main agent, which compiles the final response For example, imagine building a ticket triage system. When a new support ticket arrives, your main agent might: - Delegate to a classifier agent to determine the ticket type - Send the ticket to a priority-setting agent to determine urgency - Use a team-assignment agent to route it to the right department All this happens seamlessly without you having to write custom routing logic! Setting Up a Multi-Agent Solution The module walks you through the entire process: 1. Initializing the agents client 2. Creating connected agents with specialized roles 3. Registering them as tools for the main agent 4. Building the main agent that orchestrates the workflow 5. Running the complete system Taking It Further: Integrating MCP Tools with Azure AI Agents Once you've mastered multi-agent systems, the next level is connecting your agents to external tools and services. The Integrate MCP Tools with Azure AI Agents module teaches you how to use the Model Context Protocol (MCP) to give your agents access to a dynamic catalog of tools. What is Dynamic Tool Discovery? Traditionally, adding new tools to an AI agent meant hardcoding each one directly into your agent's code. But what if tools change frequently, or if different teams manage different tools? This approach quickly becomes unmanageable. Dynamic tool discovery through MCP solves this problem by: 1. Centralizing Tool Management: Tools are defined and managed in a central MCP server 2. Enabling Runtime Discovery: Agents discover available tools during runtime through the MCP client 3. Supporting Automatic Updates: When tools are updated on the server, agents automatically get the latest versions The MCP Server-Client Architecture The architecture involves two key components: 1. MCP Server: Acts as a registry for tools, hosting tool definitions decorated with `@mcp.tool`. Tools are exposed over HTTP when requested. 2. MCP Client: Acts as a bridge between your MCP server and Azure AI Agent. It discovers available tools, generates Python function stubs to wrap them, and registers those functions with your agent. This separation of concerns makes your AI solution more maintainable and adaptable to change. Setting Up MCP Integration The module guides you through the complete process: 1. Setting up an MCP server with tool definitions 2. Creating an MCP client to connect to the server 3. Dynamically discovering available tools 4. Wrapping tools in async functions for agent use 5. Registering the tools with your Azure AI agent Once set up, your agent can use any tool in the MCP catalog as if it were a native function, without any hardcoding required! Practical Applications for Student Developers As a student developer, how might you apply these concepts in real projects? Classroom Projects: - Build a research assistant that delegates to specialized agents for different academic subjects - Create a coding tutor that uses different agents for explaining concepts, debugging code, and suggesting improvements Hackathons: - Develop a sustainability app that uses connected agents to analyze environmental data from different sources - Create a personal finance advisor with specialized agents for budgeting, investment analysis, and financial planning Personal Portfolio Projects: - Build a content creation assistant with specialized agents for brainstorming, drafting, editing, and SEO optimization - Develop a health and wellness app that uses MCP tools to connect to fitness APIs, nutrition databases, and sleep tracking services Getting Started Ready to dive in? Both modules include hands-on exercises where you'll build real working examples: - A ticket triage system using connected agents - An inventory management assistant that integrates with MCP tools The prerequisites are straightforward: - Experience with deploying generative AI models in Azure AI Foundry - Programming experience with Python or C# Conclusion Multi-agent systems and MCP tools integration represent the next evolution in AI application development. By mastering these concepts, you'll be able to build more sophisticated, maintainable, and extensible AI solutions - skills that will make you stand out in internship applications and job interviews. The best part? These modules are designed with practical, hands-on learning in mind - perfect for student developers who learn by doing. So why not give them a try? Your future AI applications (and your resume) will thank you for it! Want to learn more about Model Context Protocol 'MCP' see MCP for Beginners Happy coding!
CampusSphere: Building the Future of Campus AI with Microsoft's Agentic Framework
Project Overview We are a team of Imperial College Students committed to improving campus life through innovative multi-agent solutions. CampusSphere leverages Microsoft Azure AI capabilities to automate core university campus services. We created an end-to-end solution that allows both students and staff to access a multi-agent framework for room/gym booking, attendance tracking, calendar management, IoT monitoring and more. 🔭 Our Initial Vision: Reimagining Campus Technology When our team at Imperial College London embarked on the CampusSphere project as part of Microsoft's Agentic Campus initiative, we had one clear ambition: to create an intelligent campus ecosystem that would fundamentally change how students, faculty, and staff interact with university services. The inspiration came from a simple observation—despite living in an age of advanced AI, campus technology remained frustratingly fragmented. Students juggled multiple portals for course registration, room booking, dining services, and academic support. Faculty members navigated separate systems for teaching, research, and administrative tasks. The result? Countless hours wasted on mundane navigation tasks that could be better spent on learning, teaching, and innovation. Our vision was ambitious: create a single, intelligent interface that could understand natural language, anticipate user needs, and seamlessly integrate with existing campus infrastructure. We didn't just want to build another campus app—we wanted to demonstrate how Microsoft's agentic AI technologies could create a truly intelligent campus companion. 🧠 Enter CampusSphere CampusSphere is an intelligent campus assistant made up of multiple AI agents, each with a specific domain of expertise — all communicating seamlessly through a centralized architecture. Think of it as a digital concierge for campus life, where your calendar, attendance, IoT data, and facility bookings are coordinated by specialized GPT-powered agents. Here’s what we built: TriageAgent – the brain of the system, using Retrieval-Augmented Generation (RAG) to understand user intent CalendarAgent – handles scheduling, bookings, and reminders AttendanceAgent – tracks check-ins automatically IoTAgent – monitors real-time sensor data from classrooms and labs GymAgent – manages access and reservations for sports facilities 30+ MCP Tools – perform SQL queries, scrape web data, and connect with external APIs All of this is built on Microsoft Azure AI, Semantic Kernel, and Model Context Protocol (MCP) — making it scalable, secure, and lightning fast. 🖥️ The Tech Stack Our Azure-powered architecture showcases a modular and scalable approach to real-time data processing and intelligent agent coordination. The frontend is built using React with a Vite development server, providing a fast and responsive user interface. When users submit a prompt, it travels to a Flask backend server acting as the Triage agent, which intelligently delegates tasks to a FastAPI agent service. This FastAPI service asynchronously communicates with individual agents and handles responses efficiently. Complex queries are routed to MCP Tools, which interact with the CosmosDB-powered Campus Database. Simultaneously, real-time synthetic IoT data is pushed into the database via Azure Function Apps and Azure IoT Hub. Authentication is securely managed: users log in through the frontend, receive a token from the database API server, and use it for authorized access to MCP services, with permissions enforced based on user roles using our custom MCP server implementation. This robust architecture enables seamless integration, real-time data flow, and secure multi-agent collaboration across Azure services. Our system leverages a multi-agent architecture designed to intelligently coordinate task execution across specialized services. At the core is the TriageAgent, which uses Retrieval-Augmented Generation (RAG) to interpret user prompts, enrich them with relevant context, and determine the optimal response path. Based on the nature of the request, it may handle the response directly, seek clarification, or delegate tasks to specific agents via FastAPI. Each specialized agent has a clearly defined role: AttendanceAgent: Interfaces with CosmosDB-backed FastAPI endpoints to check student attendance, using filters like event name, student ID, or date. IoTAgent: Monitors room conditions (e.g., temperature, CO₂ levels) and flags anomalies using real-time data from Azure IoT Hub, processed via FastAPI. CalendarAgent: Handles scheduling, availability checks, and event creation by querying or updating CosmosDB through FastAPI. Future integration with Microsoft Graph API is planned for direct calendar syncing. Gym Slot Agent: Checks available times for gym sessions using dedicated MCP tools. The triage agent serves as the orchestrator, breaking down complex requests (like "Book a gym session") into subtasks. It consults relevant agents (e.g., calendar and gym slot agents), merges results, and then confirms the final action with the user. This distributed and asynchronous workflow reduces backend load and enhances both responsiveness and reliability of the system. 🔮 What’s Next? Integrating CampusSphere with live systems via Microsoft OAuth is crucial for enhancing its capabilities. This integration will grant the agent authenticated access to a wider range of student data, moving beyond synthetic datasets. This expanded access to real-world information will enable deeply personalized advice, such as tailored course selection, scholarship recommendations, event suggestions, and deadline reminders, transforming CampusSphere into a sophisticated, proactive personal assistant. 🤝Meet the Team Behind CampusSphere Our success stemmed from a diverse team of innovators who brought together expertise from multiple domains: Benny Liu - https://www.linkedin.com/in/zong-benny-liu-393a4621b/ Lucas Ng - https://www.linkedin.com/in/lucas-ng-11b317203/ Lu Ju - https://www.linkedin.com/in/lu-ju/ Bruno Duaso - https://www.linkedin.com/in/bruno-duaso-jimeno-744464262/ Martim Coutinho - https://www.linkedin.com/in/martim-pereira-coutinho-116308233/ Krischad Pourpongpan - https://www.linkedin.com/in/krischadpua/ Yixu Pan - https://www.linkedin.com/in/yixu-pan/ Our collaborative approach enabled us to create a sophisticated agentic AI system that demonstrates the powerful potential of Microsoft's AI technologies in educational environments. 🧑💻 Project Repository: GitHub - Imperial-Microsoft-Agentic-Campus/CampusSphere Contribute to Imperial-Microsoft-Agentic-Campus/CampusSphere development by creating an account on GitHub. github.com Have questions about implementing similar solutions at your institution? Connect with our team members on LinkedIn—we're always excited to share knowledge and collaborate on innovative campus technology projects. 📚Get Started with Microsoft's AI Tools Ready to explore the technologies that made CampusSphere possible? Here are essential resources: Microsoft Semantic Kernel: The core framework for building AI agent orchestration systems. Learn how to create, coordinate, and manage multiple AI agents working together seamlessly. AI Agents for Beginners: A comprehensive guide to understanding and building AI agents from the ground up. Perfect for getting started with agentic AI development. Model Context Protocol (MCP): Learn about the protocol that enables secure connections between AI models and external tools and services—essential for building integrated AI systems. Windows AI Toolkit: Microsoft's toolkit for developing AI applications on Windows, providing local AI model development capabilities and deployment tools. Azure Container Apps: Understand how to deploy and scale containerized AI applications in the cloud, perfect for hosting multi-agent systems. Azure Cosmos DB Security: Essential security practices for managing data in AI applications, covering encryption, access control, and compliance.AI Sparks: Unleashing Agents with the AI Toolkit
The final episode of our "AI Sparks" series delved deep into the exciting world of AI Agents and their practical implementation. We also covered a fair part of MCP with Microsoft AI Toolkit extension for VS Code. We kicked off by charting the evolutionary path of intelligent conversational systems. Starting with the rudimentary rule-based Basic Chatbots, we then explored the advancements brought by Basic Generative AI Chatbots, which offered contextually aware interactions. Then we explored the Retrieval-Augmented Generation (RAG), highlighting its ability to ground generative models in specific knowledge bases, significantly enhancing accuracy and relevance. The limitations were also discussed for the above mentioned techniques. The session was then centralized to the theme – Agents and Agentic Frameworks. We uncovered the fundamental shift from basic chatbots to autonomous agents capable of planning, decision-making, and executing tasks. We moved forward with detailed discussion on the distinction between Single Agentic systems, where one core agent orchestrates the process, and Multi-Agent Architectures, where multiple specialized agents collaborate to achieve complex goals. A key part of building robust and reliable AI Agents, as we discussed, revolves around carefully considering four critical factors. Firstly, Knowledge-Providing agents with the right context is paramount for them to operate effectively and make informed decisions. Secondly, equipping agents with the necessary Actions by granting them access to the appropriate tools allows them to execute tasks and achieve desired outcomes. Thirdly, Security is non-negotiable; ensuring agents have access only to the data and services they genuinely need is crucial for maintaining privacy and preventing unintended actions. Finally, establishing robust Evaluations mechanisms is essential to verify that agents are completing tasks correctly and meeting the required standards. These four pillars – Knowledge, Actions, Security, and Evaluation – form the bedrock of any successful agentic implementation. To illustrate the transformative power of AI Agents, we explored several interesting use cases and applications. These ranged from intelligent personal assistants capable of managing schedules and automating workflows to sophisticated problem-solving systems in domains like customer service. A significant portion of the session was dedicated to practical implementation through demonstrations. We highlighted key frameworks that are empowering developers to build agentic systems.: Semantic Kernel: We highlighted its modularity and rich set of features for integrating various AI services and tools. Autogen Studio: The focus here was on its capabilities for facilitating the creation and management of multi-agent conversations and workflows. Agent Service: We discussed its role in providing a more streamlined and managed environment for deploying and scaling AI agents. The major point of attraction was that these were demonstrated using the local LLMs which were hosted using AI Toolkit. This showcased the ease with which developers can utilize VS Code AI toolkit to build and experiment with agentic workflows directly within their familiar development environment. Finally, we demystified the concept of Model Context Protocol (MCP) and demonstrated how seamlessly it can be implemented using the Agent Builder within the VS Code AI Toolkit. We demonstrated this with a basic Website development using MCP. This practical demonstration underscored the toolkit's power in simplifying the development of complex solutions that can maintain context and engage in more natural, multi-step interactions. The "AI Sparks" series concluded with a discussion, where attendees had a clearer understanding of the evolution, potential and practicalities of AI Agents. The session underscored that we are on the cusp of a new era of intelligent systems that are not just reactive but actively work alongside us to achieve goals. The tools and frameworks are maturing, and the possibilities for agentic applications are sparking innovation across various industries. It was an exciting journey, and engagement during the final session on AI Sparks around Agents truly highlighted the transformative potential of this field. "AI Sparks" Series Roadmap: The "AI Sparks" series delved deeper into specific topics using AI Toolkit for Visual Studio Code, including: Introduction to AI toolkit and feature walkthrough: Introduction to the AI Toolkit extension for VS Code a powerful way to explore and integrate the latest AI models from OpenAI, Meta, Deepseek, Mistral, and more. Introduction to SLMs and local model with use cases: Explore Small Language Models (SLMs) and how they compare to larger models. Building RAG Applications: Create powerful applications that combine the strengths of LLMs with external knowledge sources. Multimodal Support and Image Analysis: Working with vision models and building multimodal applications. Evaluation and Model Selection: Evaluate model performance and choose the best model for your needs. Agents and Agentic Frameworks: Exploring the cutting edge of AI agents and how they can be used to build more complex and autonomous systems. The full playlist of the series with all the episodes of "AI Sparks" is available at AI Sparks Playlist. Continue the discussion and questions in Microsoft AI Discord Community where we have a dedicated AI-sparks channel. All the code samples can be found on AI_Toolkit_Samples .We look forward to continuing these insightful discussions in future series!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.AI Agents: Mastering the Tool Use Design Pattern - Part 4
This blog post, Part 4 of a series on AI agents, delves into the Tool Use Design Pattern, a key concept in enabling agents to interact with external systems and perform a wider range of tasks. The post explains how tools, ranging from simple functions to complex API calls, are invoked by AI agents through model-generated function calls. Several use cases are presented, highlighting the versatility of this pattern, from dynamic information retrieval and code execution to workflow automation and customer support. The post further details the implementation of function/tool calling, including choosing a suitable LLM, defining a function schema, and writing the function code. Examples using Semantic Kernel and Azure AI Agent Service illustrate how agentic frameworks simplify tool integration. Finally, the post addresses security considerations and provides links to valuable resources, including the "AI Agents for Beginners" GitHub repository and related workshops, for further learning.AI Agents: Key Principles and Guidelines - Part 3
This blog post, the third in a series on AI agents, focuses on user-centric design principles for building effective and trustworthy agentic systems. Drawing from the "Agentic Design Patterns" section of Microsoft's "AI Agents for Beginners" GitHub repository, the post outlines key principles categorized by Agent (Space), Agent (Time), and Agent (Core). These principles emphasize connection, accessibility, leveraging historical context, adapting to future needs, and establishing trust through transparency and control. Practical implementation guidelines are provided, along with a travel agent example to illustrate how these principles can be applied in real-world scenarios. The post also links to additional resources and previous installments in the series for a comprehensive learning experience.
