Foundry IQ: Unlocking ubiquitous knowledge for agents
Introducing Foundry IQ by Azure AI Search in Microsoft Foundry. Foundry IQ is a centralized knowledge layer that connects agents to data with the next generation of retrieval-augmented generation (RAG). Foundry IQ includes the following features: Knowledge bases: Available directly in the new Foundry portal, knowledge bases are reusable, topic-centric collections that ground multiple agents and applications through a single API. Automated indexed and federated knowledge sources – Expand what data an agent can reach by connecting to both indexed and remote knowledge sources. For indexed sources, Foundry IQ delivers automatic indexing, vectorization, and enrichment for text, images, and complex documents. Agentic retrieval engine in knowledge bases – A self-reflective query engine that uses AI to plan, select sources, search, rank and synthesize answers across sources with configurable “retrieval reasoning effort.” Enterprise-grade security and governance – Support for document-level access control, alignment with existing permissions models, and options for both indexed and remote data. Foundry IQ is available in public preview through the new Foundry portal and Azure portal with Azure AI Search. Foundry IQ is part of Microsoft's intelligence layer with Fabric IQ and Work IQ.Building your own copilot – yes, but how? (Part 1 of 2)
Are you interested in building your own AI co-pilot? Check out the first of a two-part blog post from Carlotta Castelluccio that covers the basics of creating a virtual assistant that can help you with tasks like scheduling, email management, and more. Learn about the tools and technologies involved, including Microsoft's Bot Framework and Language Understanding Intelligent Service (LUIS). Whether you're a software developer or just curious about the possibilities of AI, this post is a great introduction to building your own co-pilot.*Updated on 1/06/2021* Quick Start: Setting Up Your COVID-19 Healthcare Bot
The Healthcare Bot Service is an AI-powered, extensible, secure and compliant healthcare experience. Microsoft now has a specific template pre-built for COVID-19. It takes inbound requests, asks about the patient’s symptoms, and assists in getting people access to trusted and relevant healthcare services and information based on the CDC recommendation.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 Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your Copilot Studio agent. To get started, navigate to 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'. Make sure you are in the same environment you used to create your cloud flow. 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. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting 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 blogBring all your workloads to the edge with AKS Edge Essentials. Now Generally Available!
Announcing the General Availability (GA) of Azure Kubernetes Service (AKS) Edge Essentials! AKS Edge Essentials has been developed as a managed Kubernetes for the operational edge on small-footprint devices to orchestrated workloads and drive business optimization.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.New Hugging Face Models on Azure AI: Phi-3 Variants from the Community
The Azure AI Model Catalog offers over 1.78K models, including foundation models from core partners and nearly 1.6K open-source models from the Hugging Face community. Read this post to learn about the latest models added to the Hugging Face Collection this month!New Azure Open AI models bring fast, expressive, and real‑time AI experiences in Microsoft Foundry
Modern AI applications, whether voice‑first experiences or building large software systems, rarely fit into a single prompt. Real work unfolds over time: maintaining context, following instructions, invoking tools, and adapting as requirements evolve. When these foundations break down through latency spikes, instruction drift, or unreliable tool calls, both user conversations and developer workflows are impacted. OpenAI’s latest models address this shared challenge by prioritizing continuity and reliability across real‑time interaction and long‑running engineering tasks. Starting today, GPT-Realtime-1.5, GPT-Audio-1.5, and GPT-5.3-Codex are rolling out into Microsoft Foundry. Together, these models reflect the growing needs of the modern developer and push the needle from short, stateless interactions toward AI systems that can reason, act, and collaborate over time. GPT-5.3-Codex at a glance GPT‑5.3‑Codex brings together advanced coding capability with broader reasoning and professional problem solving in a single model built for real engineering work. It unifies the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT5.2 in one system. This shifts the experience from optimizing isolated outputs to supporting longer running development efforts; where repositories are large, changes span multiple steps, and requirements aren’t always fully specified at the start. What’s improved Model experiences 25% faster execution time, according to Open AI, than its predecessors so developers can accelerate development of new applications. Built for long-running tasks that involve research, tool use, and complex, multi‑step execution while maintaining context. Midtask steerability and frequent updates allow developers to redirect and collaborate with the model as it works without losing context. Stronger computer-use capabilities allow developers to execute across the full spectrum of technical work. Common use cases Developers and teams can apply GPT‑5.3‑Codex across a wide range of scenarios, including: Refactoring and modernizing large or legacy applications Performing multi‑step migrations or upgrades Running agentic developer workflows that span analysis, implementation, testing, and remediation Automating code reviews, test generation, and defect detection Supporting development in security‑sensitive or regulated environments Pricing Model Input Price/1M Tokens Cached Input Price/1M Tokens Output Price/1M Tokens GPT-5.3-Codex $1.75 $0.175 $14.00 GPT-Realtime-1.5 and GPT-Audio-1.5 at a glance The models deliver measurable gains in reasoning and speech understanding for real‑time voice interactions on Microsoft Foundry. In OpenAI’s evaluations, it shows a +5% lift on Big Bench Audio (reasoning), a +10.23% improvement in alphanumeric transcription, and a +7% gain in instruction following, while maintaining low‑latency performance. Key improvements include: What's improved More natural‑sounding speech: Audio output is smoother and more conversational, with improved pacing and prosody. Higher audio quality: Clearer, more consistent audio output across supported voices. Improved instruction following: Better alignment with developer‑provided system and user instructions during live interactions. Function calling support: Enables structured, tool‑driven interactions within real‑time audio flows. Common use cases Developers are using GPT-Realtime-1.5 and GPT-Audio-1.5 for scenarios where low‑latency voice interaction is essential, including: Conversational voice agents for customer support or internal help desks Voice‑enabled assistants embedded in applications or devices Live voice interfaces for kiosks, demos, and interactive experiences Hands‑free workflows where audio input and output replace keyboard interaction Pricing Model Text Audio Image Input Cached Input Output Input Cached Input Output Input Cached Input Output GPT-Realtime-1.5 $4.00 $0.04 $16.0 $32.0 $0.40 $64.00 $4.00 $0.04 $16.0 GPT-Audio-1.5 $2.50 n/a $10.0 $32.00 n/a $64.00 $2.50 n/a $10.0 Getting started in Microsoft Foundry Start building in Microsoft Foundry, evaluate performance, and explore Azure Open AI models today. Foundry brings evaluation, deployment, and governance into a single workflow, helping teams progress from experiments to scalable applications while maintaining security and operational controls.
