I want to show my agent a picture—Can I?
Welcome to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a question inspired by real conversations in the AI developer community, offering practical advice and tips. To kick things off, we’re tackling a common challenge for anyone experimenting with multimodal agents: working with image input. Let’s dive in! Dear Agent Support, I’m building an AI agent, and I’d like to include screenshots or product photos as part of the input. But I’m not sure if that’s even possible, or if I need to use a different kind of model altogether. Can I actually upload an image and have the agent process it? Great question, and one that trips up a lot of people early on! The short answer is: yes, some models can process images—but not all of them. Let’s break that down a bit. 🧠 Understanding Image Input When we talk about image input or image attachments, we’re talking about the ability to send a non-text file (like a .png, .jpg, or screenshot) into your prompt and have the model analyze or interpret it. That could mean describing what’s in the image, extracting text from it, answering questions about a chart, or giving feedback on a design layout. 🚫 Not All Models Support Image Input That said, this isn’t something every model can do. Most base language models are trained on text data only, they’re not designed to interpret non-text inputs like images. In most tools and interfaces, the option to upload an image only appears if the selected model supports it, since platforms typically hide or disable features that aren't compatible with a model's capabilities. So, if your current chat interface doesn’t mention anything about vision or image input, it’s likely because the model itself isn’t equipped to handle it. That’s where multimodal models come in. These are models that have been trained (or extended) to understand both text and images, and sometimes other data types too. Think of them as being fluent in more than one language, except in this case, one of those “languages” is visual. 🔎 How to Find Image-Supporting Models If you’re trying to figure out which models support images, the AI Toolkit is a great place to start! The extension includes a built-in Model Catalog where you can filter models by Feature—like Image Attachment—so you can skip the guesswork. Here’s how to do it: Open the Model Catalog from the AI Toolkit panel in Visual Studio Code. Click the Feature filter near the search bar. Select Image Attachment. Browse the filtered results to see which models can accept visual input. Once you've got your filtered list, you can check out the model details or try one in the Playground to test how it handles image-based prompts. 🧪 Test Before You Build Before you plug a model into your agent and start wiring things together, it’s a good idea to test how the model handles image input on its own. This gives you a quick feel for the model’s behavior and helps you catch any limitations before you're deep into building. You can do this in the Playground, where you can upload an image and pair it with a simple prompt like: “Describe the contents of this image.” OR “Summarize what’s happening in this screenshot.” If the model supports image input, you’ll be able to attach a file and get a response based on its visual analysis. If you don’t see the option to upload an image, double-check that the model you’ve selected has image capabilities—this is usually a model issue, not a UI bug. 🔁 Recap Here’s a quick rundown of what we covered: Not all models support image input—you’ll need a multimodal model specifically built to handle visual data. Most platforms won’t let you upload an image unless the model supports it, so if you don’t see that option, it’s probably a model limitation. You can use the AI Toolkit’s Model Catalog to filter models by capability—just check the box for Image Attachment. Test the model in the Playground before integrating it into your agent to make sure it behaves the way you expect. 📺 Want to Go Deeper? Check out my latest video on how to choose the right model for your agent—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. And if you’re looking to sharpen your model instincts, don’t miss Model Mondays—a weekly series that helps developers like you build your Model IQ, one spotlight at a time. Whether you’re just starting out or already building AI-powered apps, it’s a great way to stay current and confident in your model choices. 👉 Explore the series and catch the next episode: aka.ms/model-mondays/rsvp If you're just getting started with building agents, check out our Agents for Beginners curriculum. And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whatever you're building, the right model is out there—and with the right tools, you'll know exactly how to find it.Impariamo a conoscere MCP: Introduzione al Model Context Protocol (MCP)
Non perderti il prossimo evento “Let’s Learn – MCP” su Microsoft Reactor il 24 di Luglio, pensato per chiunque voglia conoscere meglio il nuovo standard per agenti intelligenti (il Model Context Protocol) e imparare a metterlo in pratica. La sessione è in Italiano e le demo sono in Python, ma fa parte di una serie di live-streaming disponibili in tantissime lingue.AI Repo of the Week: Generative AI for Beginners with JavaScript
Introduction Ready to explore the fascinating world of Generative AI using your JavaScript skills? This week’s featured repository, Generative AI for Beginners with JavaScript, is your launchpad into the future of application development. Whether you're just starting out or looking to expand your AI toolbox, this open-source GitHub resource offers a rich, hands-on journey. It includes interactive lessons, quizzes, and even time-travel storytelling featuring historical legends like Leonardo da Vinci and Ada Lovelace. Each chapter combines narrative-driven learning with practical exercises, helping you understand foundational AI concepts and apply them directly in code. It’s immersive, educational, and genuinely fun. What You'll Learn 1. 🧠 Foundations of Generative AI and LLMs Start with the basics: What is generative AI? How do large language models (LLMs) work? This chapter lays the groundwork for how these technologies are transforming JavaScript development. 2. 🚀 Build Your First AI-Powered App Walk through setting up your environment and creating your first AI app. Learn how to configure prompts and unlock the potential of AI in your own projects. 3. 🎯 Prompt Engineering Essentials Get hands-on with prompt engineering techniques that shape how AI models respond. Explore strategies for crafting prompts that are clear, targeted, and effective. 4. 📦 Structured Output with JSON Learn how to guide the model to return structured data formats like JSON—critical for integrating AI into real-world applications. 5. 🔍 Retrieval-Augmented Generation (RAG) Go beyond static prompts by combining LLMs with external data sources. Discover how RAG lets your app pull in live, contextual information for more intelligent results. 6. 🛠️ Function Calling and Tool Use Give your LLM new powers! Learn how to connect your own functions and tools to your app, enabling more dynamic and actionable AI interactions. 7. 📚 Model Context Protocol (MCP) Dive into MCP, a new standard for organizing prompts, tools, and resources. Learn how it simplifies AI app development and fosters consistency across projects. 8. ⚙️ Enhancing MCP Clients with LLMs Build on what you’ve learned by integrating LLMs directly into your MCP clients. See how to make them smarter, faster, and more helpful. ✨ More chapters coming soon—watch the repo for updates! Companion App: Interact with History Experience the power of generative AI in action through the companion web app—where you can chat with historical figures and witness how JavaScript brings AI to life in real time. Conclusion Generative AI for Beginners with JavaScript is more than a course—it’s an adventure into how storytelling, coding, and AI can come together to create something fun and educational. Whether you're here to upskill, experiment, or build the next big thing, this repository is your all-in-one resource to get started with confidence. 🔗 Jump into the future of development—check out the repo and start building with AI today!
Orchestrate multimodal AI insights within your healthcare data estate (Public Preview)
In today’s healthcare landscape, there is an increasing emphasis on leveraging artificial intelligence (AI) to extract meaningful insights from diverse datasets to improve patient care and drive clinical research. However, incorporating AI into your healthcare data estate often brings significant costs and challenges, especially when dealing with siloed and unstructured data. Healthcare organizations produce and consume data that is not only vast but also varied in format—ranging from structured EHR entries to unstructured clinical notes and imaging data. Traditional methods require manual effort to prepare and harmonize this data for AI, specify the AI output format, set up API calls, store the AI outputs, integrate the AI outputs, and analyze the AI outputs for each AI model or service you decide to use. Orchestrate multimodal AI insights is designed to streamline and scale healthcare AI within your data estate by building off of the data transformations in healthcare data solutions in Microsoft Fabric. This capability provides a framework to generate AI insights by connecting your multimodal healthcare data to an ecosystem of AI services and models and integrating structured AI-generated insights back into your data estate. When you combine these AI-generated insights with the existing healthcare data in your data estate, you can power advanced analytics scenarios for your organization and patient population. Key features: Metadata store lakehouse acts as a central repository for the metadata for AI orchestration to effectively capture and manage enrichment definitions, view definitions, and contextual information for traceability purposes. Execution notebooks define the enrichment view and enrichment definition based on the model configuration and input mappings. They also specify the model processor and transformer. The model processor calls the model API, and the transformer produces the standardized output while saving the output in the bronze lakehouse in the Ingest folder. Transformation pipeline to ingest AI-generated insights through the healthcare data solutions medallion lakehouse layers and persist the insights in an enrichment store within the silver layer. Conceptual architecture: The data transformations in healthcare data solutions in Microsoft Fabric allow you ingest, store, and analyze multimodal data. With the orchestrate multimodal AI insights capability, this standardized data serves as the input for healthcare AI models. The model results are stored in a standardized format and provide new insights from your data. The diagram below shows the flow of integrating AI generated insights into the data estate, starting as raw data in the bronze lakehouse and being transformed to delta tables in the silver lakehouse. This capability simplifies AI integration across modalities for data-driven research and care, currently supporting: Text Analytics for health in Azure AI Language to extract medical entities such as conditions and medications from unstructured clinical notes. This utilizes the data in the DocumentReference FHIR resource. MedImageInsight healthcare AI model in Azure AI Foundry to generate medical image embeddings from imaging data. This model leverages the data in the ImagingStudy FHIR resource. MedImageParse healthcare AI model in Azure AI Foundry to enable segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities. This model uses the data in the ImagingStudy FHIR resource. By using orchestrate multimodal AI insights to leverage the data in healthcare data solutions for these models and integrate the results into the data estate, you can analyze your existing data alongside AI enrichments. This allows you to explore use cases such as creating image segmentations and combining with your existing imaging metadata and clinical data to enable quick insights and disease progression trends for clinical research at the patient level. Get started today! This capability is now available in public preview, and you can use the in-product sample data to test this feature with any of the three models listed above. For more information and to learn how to deploy the capability, please refer to the product documentation. We will dive deeper into more detailed aspects of the capability, such as the enrichment store and custom AI use cases, in upcoming blogs. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations. FHIR® is the registered trademark of HL7 and is used with permission of HL7.Unleashing the Power of AI Agents: Transforming Business Operations
Let "Get Started with AI Agents," in this short blog I want explore the evolution, capabilities, and applications of AI agents, highlighting their potential to enhance productivity and efficiency. We take a peak into the challenges of developing AI agents and introduce powerful tools like Azure AI Foundry and Azure AI Agent Service that empower developers to build, deploy, and scale AI agents securely and efficiently. In today's rapidly evolving technological landscape, the integration of AI agents into business processes is becoming increasingly essential. Lets delve into the transformative potential of AI agents and how they can revolutionize various aspects of our operations. We begin by exploring the evolution of LLM-based solutions, tracing the journey from no agents to sophisticated multi-agent systems. This progression highlights the growing complexity and capabilities of AI agents, which are now poised to handle wide-scope, complex use cases requiring diverse skills. Lets now look at agentic AI capabilities. AI agents can significantly enhance employee productivity and process efficiency, making our operations faster and more effective. Lets examine the key applications of AI agents across industries, such as travel booking and expense management, employee onboarding, personalized customer support, and data analytics and reporting. However, developing AI agents is not without its challenges. Some of the primary considerations, including tool integration, interoperability, scalability, real-time processing, maintenance, flexibility, error handling, and security. These challenges underscore the need for robust platforms that enable rapid development and secure deployment of AI agents. To this end, we introduce Azure AI Foundry and Azure AI Agent Service. These tools empower developers to build, deploy, and scale AI agents securely and efficiently. Azure AI Foundry offers a comprehensive suite of tools, including model catalogs, content safety features, and machine learning capabilities. The Azure AI Agent Service, currently in public preview, provides flexible model selection, extensive data connections, enterprise-grade security, and rapid development and automation capabilities. When building multi agent or agentic based systems there is a huge importance of multi-agent orchestration. Tools like AutoGen and Semantic Kernel facilitate the orchestration of multi-agent systems, enabling seamless integration and collaboration between different AI agents. In conclusion, the transformative potential of AI agents in driving productivity, efficiency, and innovation. By leveraging the capabilities of Azure AI Foundry and Azure AI Agent Service, we can overcome the challenges of AI agent development and unlock new opportunities for growth and success. Resources Azure AI Discord - https://aka.ms/AzureAI/Discord Global AI community - https://globalai.community Generative AI for beginners – https://aka.ms/genai-beginners AI Agents for beginners - https://aka.ms/ai-agents-beginners Attend one of the Global AI Bootcamp near you - https://globalai.community/bootcamp/ Build AI Tour open content - https://aka.ms/aitour/repos Build your first Agent with Azure AI Agent Service - Slide deck and code - https://github.com/microsoft/aitour-build-your-first-agent-with-azure-ai-agent-service
Is AI Foundry in new exam for DP-100
A 25-30% of the DP-100 exam is now dedicated to Optimizing Language Models for AI Applications - is this requiring Azure AI Foundry? It doesn't say specifically in the study guide: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 Also, the videos could benefit from being updated to cover the changes as of 16 January 2025.Tiny But Mighty: Unleashing the Power of Small Language Models 🚀
While Large Language Models (LLMs) like GPT-4 dominate headlines with their extensive capabilities, they often come at the cost of high computational requirements and complexity. For developers and organizations looking to implement AI solutions on edge devices or with limited resources, Small Language Models (SLMs) are emerging as a practical alternative. SLMs are not just "smaller" versions of their larger counterparts—they're designed to be faster, more efficient, and adaptable for specific tasks. With fewer parameters and lower computational needs, SLMs open the door to deploying AI on mobile devices, IoT systems, and edge environments without compromising performance. What You Stand to Learn 🧠 Introduction to Microsoft's AI Ecosystem Discover Microsoft's end-to-end AI development tools, from Azure AI Services to ONNX Runtime, enabling efficient and secure deployment of AI models across cloud and edge environments. The Advantages of SLMs over LLMs SLMs are game-changers for edge AI applications, providing faster training and inference times, reduced energy costs, and scalability across diverse devices. Hands-On with Phi-3 and ONNX Runtime Experience live demonstrations of SLMs in action with tools like Phi-3 and ONNX Runtime, showcasing how to fine-tune and deploy models on mobile devices, IoT, and hybrid cloud environments. Responsible AI Practices Understand how to safeguard your AI applications with Microsoft's Responsible AI toolkit, ensuring ethical and trustworthy deployments. Watch the Full Session 👨💻 📅 Date: December 12, 2024 ⏰ Time: 4 PM GMT | 5 PM CEST | 8 AM PT | 11 AM ET | 7 PM EAT A session packed with live demos, practical examples, and Q&A opportunities. Register NOW | Events | Microsoft Reactor Agenda 🔍 Introduction (5 min) A brief overview of the session and its focus on SLMs and LLMs. Microsoft AI Tooling (5 min) Explore the latest tools like Azure AI Services, Azure Machine Learning, and Responsible AI Tooling. How to Choose the Right Model (10 min) Key considerations such as performance, customizability, and ethical implications. Comparing SLMs vs LLMs (10 min) The strengths, weaknesses, and best use cases for both Small and Large Language Models. Deploying Models at the Edge (10 min) Insights into optimizing AI for mobile, IoT, and edge devices. Q&A Addressing participant questions about AI development and deployment.AI Toolkit for VS Code October Update
We're thrilled to bring you the October update for the AI Toolkit for Visual Studio Code! This month marks another major milestone with version 0.24.0, introducing groundbreaking GitHub Copilot Tools Integration and additional user experience enhancements that make AI-powered development more seamless than ever. Let's dive into what's new! 👇 🚀 GitHub Copilot Tools Integration We are excited to announce the integration of GitHub Copilot Tools into AI Toolkit for VS Code. This integration empowers developers to build AI-powered applications more efficiently by leveraging Copilot's capabilities enhanced by AI Toolkit. 🤖 AI Agent Code Generation Tool This powerful tool provides best practices, guidance, steps, and code samples on Microsoft Agent Framework for GitHub Copilot to better scaffold AI agent applications. Whether you're building your first agent or scaling complex multi-agent systems, this tool ensures you follow the latest best practices and patterns. 📊 AI Agent Evaluation Planner Tool Building great AI agents requires thorough evaluation. This tool guides users through the complete process of evaluating AI agents, including: Defining evaluation metrics - Establish clear success criteria for your agents Creating evaluation datasets - Generate comprehensive test datasets Analyzing results - Understand your agent's performance and areas for improvement The Evaluation Planner works seamlessly with two specialized sub-tools: 🏃♂️ Evaluation Agent Runner Tool This tool runs agents on provided datasets and collects results, making it easy to test your agents at scale across multiple scenarios and use cases. 💻 Evaluation Code Generation Tool Get best practices, guidance, steps, and code samples on Azure AI Foundry Evaluation Framework for GitHub Copilot to better scaffold code for evaluating AI agents. 🎯 Easy Access and Usage You can access these powerful tools in two convenient ways: Direct GitHub Copilot Integration: Simply enter prompts like: Create an AI agent using Microsoft Agent Framework to help users plan a trip to Paris. Evaluate the performance of my AI agent using Azure AI Foundry Evaluation Framework. AI Toolkit Tree View: For quick access, find these tools in the AI Toolkit Tree View UI under the section `Build Agent with GitHub Copilot`. ✨ Additional Enhancements 🎨 Model Playground Improvements The user experience in Model Playground has been significantly enhanced: Resizable Divider: The divider between chat output and model settings is now resizable, allowing you to customize your workspace layout for better usability and productivity. 📚 Model Catalog Updates We've unified and streamlined the model discovery experience: Unified Local Models: The ONNX models section in the Model Catalog has been merged with Foundry Local Models on macOS and Windows platforms, providing a unified experience for discovering and selecting local models. Simplified Navigation: Find all your local model options in one place, making it easier to compare and select the right model for your use case. ## 🌟 Why This Release Matters Version 0.24.0 represents a significant step forward in making AI development more accessible and efficient: Seamless Integration: The deep integration with GitHub Copilot means AI best practices are now available right where you're already working. End-to-End Workflow: From agent creation to evaluation, you now have comprehensive tooling that guides you through the entire AI development lifecycle. Enhanced Productivity: Improved UI elements and unified experiences reduce friction and help you focus on building great AI applications. 🚀 Get Started and Share Your Feedback Ready to experience the future of AI development? Here's how to get started: 📥 Download: Install the AI Toolkit from the Visual Studio Code Marketplace 📖 Learn: Explore our comprehensive AI Toolkit Documentation 🔍 Discover: Check out the complete changelog for v0.24.0 We'd love to hear from you! Whether it's a feature request, bug report, or feedback on your experience, join the conversation and contribute directly on our GitHub repository. 🎯 What's Next? This release sets the foundation for even more exciting developments ahead. The GitHub Copilot Tools Integration opens up new possibilities for AI-assisted development, and we're just getting started. Stay tuned for more updates, and let's continue building the future of AI agent development together! 💡💬 Happy coding, and see you next month! 🚀
Building a Multi-Agent System with Azure AI Agent Service: Campus Event Management
Personal Background My name is Peace Silly. I studied French and Spanish at the University of Oxford, where I developed a strong interest in how language is structured and interpreted. That curiosity about syntax and meaning eventually led me to computer science, which I came to see as another language built on logic and structure. In the academic year 2024–2025, I completed the MSc Computer Science at University College London, where I developed this project as part of my Master’s thesis. Project Introduction Can large-scale event management be handled through a simple chat interface? This was the question that guided my Master’s thesis project at UCL. As part of the Industry Exchange Network (IXN) and in collaboration with Microsoft, I set out to explore how conversational interfaces and autonomous AI agents could simplify one of the most underestimated coordination challenges in campus life: managing events across multiple departments, societies, and facilities. At large universities, event management is rarely straightforward. Rooms are shared between academic timetables, student societies, and one-off events. A single lecture theatre might host a departmental seminar in the morning, a society meeting in the afternoon, and a careers talk in the evening, each relying on different systems, staff, and communication chains. Double bookings, last-minute cancellations, and maintenance issues are common, and coordinating changes often means long email threads, manual spreadsheets, and frustrated users. These inefficiencies do more than waste time; they directly affect how a campus functions day to day. When venues are unavailable or notifications fail to reach the right people, even small scheduling errors can ripple across entire departments. A smarter, more adaptive approach was needed, one that could manage complex workflows autonomously while remaining intuitive and human for end users. The result was the Event Management Multi-Agent System, a cloud-based platform where staff and students can query events, book rooms, and reschedule activities simply by chatting. Behind the scenes, a network of Azure-powered AI agents collaborates to handle scheduling, communication, and maintenance in real time, working together to keep the campus running smoothly. The user scenario shown in the figure below exemplifies the vision that guided the development of this multi-agent system. Starting with Microsoft Learning Resources I began my journey with Microsoft’s tutorial Build Your First Agent with Azure AI Foundry which introduced the fundamentals of the Azure AI Agent Service and provided an ideal foundation for experimentation. Within a few weeks, using the Azure Foundry environment, I extended those foundations into a fully functional multi-agent system. Azure Foundry’s visual interface was an invaluable learning space. It allowed me to deploy, test, and adjust model parameters such as temperature, system prompts, and function calling while observing how each change influenced the agents’ reasoning and collaboration. Through these experiments, I developed a strong conceptual understanding of orchestration and coordination before moving to the command line for more complex development later. When development issues inevitably arose, I relied on the Discord support community and the GitHub forum for troubleshooting. These communities were instrumental in addressing configuration issues and providing practical examples, ensuring that each agent performed reliably within the shared-thread framework. This early engagement with Microsoft’s learning materials not only accelerated my technical progress but also shaped how I approached experimentation, debugging, and iteration. It transformed a steep learning curve into a structured, hands-on process that mirrored professional software development practice. A Decentralised Team of AI Agents The system’s intelligence is distributed across three specialised agents, powered by OpenAI’s GPT-4.1 models through Azure OpenAI Service. They each perform a distinct role within the event management workflow: Scheduling Agent – interprets natural language requests, checks room availability, and allocates suitable venues. Communications Agent – notifies stakeholders when events are booked, modified, or cancelled. Maintenance Agent – monitors room readiness, posts fault reports when venues become unavailable, and triggers rescheduling when needed. Each agent operates independently but communicates through a shared thread, a transparent message log that serves as the coordination backbone. This thread acts as a persistent state space where agents post updates, react to changes, and maintain a record of every decision. For example, when a maintenance fault is detected, the Maintenance Agent logs the issue, the Scheduling Agent identifies an alternative venue, and the Communications Agent automatically notifies attendees. These interactions happen autonomously, with each agent responding to the evolving context recorded in the shared thread. Interfaces and Backend The system was designed with both developer-focused and user-facing interfaces, supporting rapid iteration and intuitive interaction. The Terminal Interface Initially, the agents were deployed and tested through a terminal interface, which provided a controlled environment for debugging and verifying logic step by step. This setup allowed quick testing of individual agents and observation of their interactions within the shared thread. The Chat Interface As the project evolved, I introduced a lightweight chat interface to make the system accessible to staff and students. This interface allows users to book rooms, query events, and reschedule activities using plain language. Recognising that some users might still want to see what happens behind the scenes, I added an optional toggle that reveals the intermediate steps of agent reasoning. This transparency feature proved valuable for debugging and for more technical users who wanted to understand how the agents collaborated. When a user interacts with the chat interface, they are effectively communicating with the Scheduling Agent, which acts as the primary entry point. The Scheduling Agent interprets natural-language commands such as “Book the Engineering Auditorium for Friday at 2 PM” or “Reschedule the robotics demo to another room.” It then coordinates with the Maintenance and Communications Agents to complete the process. Behind the scenes, the chat interface connects to a FastAPI backend responsible for core logic and data access. A Flask + HTMX layer handles lightweight rendering and interactivity, while the Azure AI Agent Service manages orchestration and shared-thread coordination. This combination enables seamless agent communication and reliable task execution without exposing any of the underlying complexity to the end user. Automated Notifications and Fault Detection Once an event is scheduled, the Scheduling Agent posts the confirmation to the shared thread. The Communications Agent, which subscribes to thread updates, automatically sends notifications to all relevant stakeholders by email. This ensures that every participant stays informed without any manual follow-up. The Maintenance Agent runs routine availability checks. If a fault is detected, it logs the issue to the shared thread, prompting the Scheduling Agent to find an alternative room. The Communications Agent then notifies attendees of the change, ensuring minimal disruption to ongoing events. Testing and Evaluation The system underwent several layers of testing to validate both functional and non-functional requirements. Unit and Integration Tests Backend reliability was evaluated through unit and integration tests to ensure that room allocation, conflict detection, and database operations behaved as intended. Automated test scripts verified end-to-end workflows for event creation, modification, and cancellation across all agents. Integration results confirmed that the shared-thread orchestration functioned correctly, with all test cases passing consistently. However, coverage analysis revealed that approximately 60% of the codebase was tested, leaving some areas such as Azure service integration and error-handling paths outside automated validation. These trade-offs were deliberate, balancing test depth with project scope and the constraints of mocking live dependencies. Azure AI Evaluation While functional testing confirmed correctness, it did not capture the agents’ reasoning or language quality. To assess this, I used Azure AI Evaluation, which measures conversational performance across metrics such as relevance, coherence, fluency, and groundedness. The results showed high scores in relevance (4.33) and groundedness (4.67), confirming the agents’ ability to generate accurate and context-aware responses. However, slightly lower fluency scores and weaker performance in multi-turn tasks revealed a retrieval–execution gap typical in task-oriented dialogue systems. Limitations and Insights The evaluation also surfaced several key limitations: Synthetic data: All tests were conducted with simulated datasets rather than live campus systems, limiting generalisability. Scalability: A non-functional requirement in the form of horizontal scalability was not tested. The architecture supports scaling conceptually but requires validation under heavier load. Despite these constraints, the testing process confirmed that the system was both technically reliable and linguistically robust, capable of autonomous coordination under normal conditions. The results provided a realistic picture of what worked well and what future iterations should focus on improving. Impact and Future Work This project demonstrates how conversational AI and multi-agent orchestration can streamline real operational processes. By combining Azure AI Agent Services with modular design principles, the system automates scheduling, communication, and maintenance while keeping the user experience simple and intuitive. The architecture also establishes a foundation for future extensions: Predictive maintenance to anticipate venue faults before they occur. Microsoft Teams integration for seamless in-chat scheduling. Scalability testing and real-user trials to validate performance at institutional scale. Beyond its technical results, the project underscores the potential of multi-agent systems in real-world coordination tasks. It illustrates how modularity, transparency, and intelligent orchestration can make everyday workflows more efficient and human-centred. Acknowledgements What began with a simple Microsoft tutorial evolved into a working prototype that reimagines how campuses could manage their daily operations through conversation and collaboration. This was both a challenging and rewarding journey, and I am deeply grateful to Professor Graham Roberts (UCL) and Professor Lee Stott (Microsoft) for their guidance, feedback, and support throughout the project.
