Integrate Custom Azure AI Agents with CoPilot Studio and M365 CoPilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Build a Flow in Power Platform: Move into the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your CoPilot Studio solutions? Check out this blogAI Avatars: Redefining Human-Digital Interaction in the Enterprise Era
In today’s AI-driven world, businesses are constantly seeking innovative ways to humanize digital experiences. AI Avatars are emerging as a powerful solution—bridging the gap between intelligent automation and authentic, human-like engagement. With advancements in speech synthesis, large language models, and avatar rendering technologies, organizations can now deploy AI-powered digital assistants that not only understand and respond but also interact with a lifelike presence. The Rise of AI Avatars in Enterprise Applications AI Avatars go beyond traditional chatbots or voice assistants. These virtual beings offer multimodal interaction—combining voice, visual cues, and conversational intelligence into a seamless user experience. Built on enterprise-grade platforms like Azure AI, these avatars can be integrated into customer support portals, digital kiosks, internal knowledge hubs, and more. Their utility spans a range of industries: Retail: Personalized shopping assistants that guide consumers through products. Healthcare: Virtual health concierges that help patients navigate care. Education: Interactive tutors that deliver lessons with empathy and responsiveness. HR and Training: Onboarding avatars that answer employee questions, onboard new hires, or provide compliance updates. One of our key partners, Cloudforce, has integrated AI Avatar technology directly into their flagship platform nebulaONE®. This integration enables enterprises to deploy digital assistants that are deeply embedded in business processes, offering contextualized support and real-time engagement. From training and onboarding to employee self-service, nebulaONE's agentic AI Avatars act as a digital bridge between users and systems—driving efficiency, engagement, and satisfaction. Partner Spotlight: Cloudforce’s Avatar Initiative To operationalize and productize AI Avatars, Microsoft collaborates with a growing ecosystem of partners. Cloudforce is one of the early pioneers in this space. Their work in embedding avatars into nebulaONE demonstrates what’s possible when advanced AI meets real-world enterprise needs. With a vision to transform user interaction across industries, Cloudforce built a production-grade AI Avatar module designed to support customer Q&A, knowledge discovery, and live guided walkthroughs. Leveraging Azure OpenAI, Azure AI Speech, and privately-deployed secure cloud infrastructure, they have brought conversational intelligence to life—with both a face and a voice. Looking ahead, Cloudforce’s broader vision is to bring AI Avatar capabilities to millions of students—delivering immersive learning experiences that blend interactivity, personalization, and scale. Their education-focused roadmap enhancements highlight the potential of avatars not just as productivity agents, but as accessible and empathetic digital educators, delivering equitable access to knowledge previously reserved for a fortunate few. This kind of partner innovation illustrates how AI Avatars can be customized and scaled to deliver tangible business value across multiple domains. Partner Contribution "Students are already embracing generative AI at a pace and proficiency that far exceeds many professional audiences. With Azure's AI Avatar technology, educators and institutions can tailor unique GenAI interactions that promote reasoning and learning over simply receiving answers the way they would with common public bots." says Husein Sharaf, Founder and CEO at Cloudforce. "We understand the concerns and hesitation that our education partners are currently grappling with, however we believe they can and should take an active role in shaping how this transformative technology is leveraged across their campuses, or risk being left behind as students choose their own adventure." "Microsoft's enterprise AI capabilities are enabling partners like us to deliver secure, cost-efficient, and responsible AI experiences at scale. With the Azure AI Foundry and key innovations like AI Avatars as our building blocks, the nebulaONE platform is poised to serve as the GenAI gateway to tens of thousands of business users, and millions of students at leading educational institutions globally. Our customers are seeking unique differentiators that will enable them to compete and win in the age of AI, and our collaboration with Microsoft is empowering us to deliver just that." Summary AI Avatars represent the next frontier in digital interaction. By combining conversational AI, expressive voice synthesis, and realistic visual rendering, these intelligent agents deliver truly human-like experiences—at scale. They are not just tools, but digital extensions of your brand. Partners like Cloudforce are leading the way with innovative platforms like nebulaONE, showing how this technology can be embedded into enterprise solutions and educational experiences to drive efficiency with a human touch. While Cloudforce is among the first to productize AI Avatars using Azure AI, they are part of a growing movement—helping to shape the future of AI-powered experiences across industries. As AI continues to evolve, avatars will become a standard interface—transforming the way we learn, work, and engage with digital systems.The Future of AI: Computer Use Agents Have Arrived
Discover the groundbreaking advancements in AI with Computer Use Agents (CUAs). In this blog, Marco Casalaina shares how to use the Responses API from Azure OpenAI Service, showcasing how CUAs can launch apps, navigate websites, and reason through tasks. Learn how CUAs utilize multimodal models for computer vision and AI frameworks to enhance automation. Explore the differences between CUAs and traditional Robotic Process Automation (RPA), and understand how CUAs can complement RPA systems. Dive into the future of automation and see how CUAs are set to revolutionize the way we interact with technology.The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.The Future of AI: Unleashing the Potential of AI Translation
The Co-op Translator automates the translation of markdown files and text within images using Azure AI Foundry. This open-source tool leverages advanced Large Language Model (LLM) technology through Azure OpenAI Services and Azure AI Vision to provide high-quality translations. Designed to break language barriers, the Co-op Translator features an easy-to-use command line interface and Python package, making technical content globally accessible with minimal manual effort.The Future of AI: Customizing AI agents with the Semantic Kernel agent framework
The blog post Customizing AI agents with the Semantic Kernel agent framework discusses the capabilities of the Semantic Kernel SDK, an open-source tool developed by Microsoft for creating AI agents and multi-agent systems. It highlights the benefits of using single-purpose agents within a multi-agent system to achieve more complex workflows with improved efficiency. The Semantic Kernel SDK offers features like telemetry, hooks, and filters to ensure secure and responsible AI solutions, making it a versatile tool for both simple and complex AI projects.The Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.The Future of AI: Power Your Agents with Azure Logic Apps
Building intelligent applications no longer requires complex coding. With advancements in technology, you can now create agents using cloud-based tools to automate workflows, connect to various services, and integrate business processes across hybrid environments without writing any code.The Future Of AI: Deconstructing Contoso Chat - Learning GenAIOps in practice
How can AI engineers build applied knowledge for GenAIOps practices? By deconstructing working samples! In this multi-part series, we deconstruct Contoso Chat (a RAG-based retail copilot sample) and use it to learn the tools and workflows to streamline out end-to-end developer journey using Azure AI Foundry.The Future of AI: Horses for Courses - Task-Specific Models and Content Understanding
Task-specific models are designed to excel at specific use cases, offering highly specialized solutions that can be more efficient and cost-effective than general-purpose models. These models are optimized for particular tasks, resulting in faster performance and lower latency, and they often do not require prompt engineering or fine-tuning.
