Create agentic solutions quickly and efficiently with Azure AI Foundry. Choose the right models, ground your agents with knowledge, and seamlessly integrate AI into your development workflow — from early experimentation to production. Test, optimize, and deploy with built-in evaluation and management tools. See how to leverage the Azure AI Foundry SDK to code and orchestrate intelligent agents, monitor performance with tracing and assessments, and streamline DevOps with production-ready management.
\n\nYina Arenas, from the Azure AI Foundry team, shares its extensive capabilities as a unified platform that supports you throughout the entire AI development lifecycle.
\nThe model catalog in Azure AI Foundry gives you access to thousands of AI models, including top-tier LLMs & specialized models, with optimizations for cloud & edge deployment. Take a look.
\nWork seamlessly with Azure AI Foundry inside VS Code, GitHub, and Copilot Studio. See how to integrate AI into your dev workflow.
\nAutomate tasks like research, writing, editing, and communication using one system. Get started with Azure AI Foundry.
\n00:00 — Create agentic solutions with Azure AI Foundry
\n00:20 — Model catalog in Azure AI Foundry
\n02:15 — Experiment in the Azure AI Foundry playground
\n03:10 — Create and customize agents
\n04:13 — Assess and improve agents
\n05:58 — Monitor and manage apps
\n06:50 — Create a multi-agentic app in code
\n09:26 — Create a Sender agent
\n10:39 — How to connect orchestration logic
\n11:25 — Watch agents work
\n12:26 — Wrap up
\nGet started with Azure AI Foundry at https://ai.azure.com
\nAs Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.
\n-If you’re looking to create agentic solutions and want to move quickly and efficiently, Azure AI Foundry is the one place for discovering and accessing the right building blocks for your agents, with everything you need for AI development. Today, I’ll share the essentials for Azure AI Foundry, starting with a tour of its extensive capabilities as a unified platform that supports you throughout the entire AI development lifecycle; from initial concept with early experimentation, coding in your preferred ID, pre-production assessment, management in production, and beyond, followed by a real example of the steps for creating a multi-agent application using the new Azure AI Agents service in Azure AI Foundry, all integrated with your code. Starting with our tour, you can easily reach to Azure AI Foundry at ai.azure.com, and once you’ve created a project, panning down the left rail, you can quickly see the core experiences.
\n-First, the model catalog helps you discover and access a growing collection of thousands of models to power your individual agents as you build your system, including premium large language models from OpenAI, Meta, DeepSeek, Cohere, and more, as well as small language models like Microsoft Phi. And of course, there are hundreds of open models like those from Hugging Face for you to try out. Models are also available by area of specialization.
\n-For example, there are regional and focused models to support interactions with different spoken languages, like Mistral for European languages and Jais for Arabic. And separately, there are industry-specific models that you can choose from. The entire model catalog is hosted on Microsoft’s supercomputer infrastructure in Azure for optimized cost performance. Next, in terms of model deployment, you can choose to run models on hosted hardware with managed compute, and for those of our popular premium models, you can use our serverless API option. As you use Azure AI Foundry, you can of course also bring your own models to run on your Azure infrastructure.
\n-Then, to help you choose the right model for your agent, you can easily experiment in our playground. For agents, you can add knowledge to ground your model. You can choose files to upload, use existing search index, or add web knowledge using Microsoft Bing. There are also options to add data from Microsoft Fabric as well as SharePoint to connect with data in Microsoft 365. You can also define actions for your agents to perform, like calling APIs, functions, or using Code interpreter to write and run Python code to automate processes. And back on the homepage, clicking into AI Services provide additional task-specific capabilities that you can use to augment your agents, like speech, translation, vision, and content safety. So right from the start on your application design, you can leverage Azure AI Foundry to evaluate AI models and services for your application.
\n-Next, to create and customize agents, the new Azure AI Agents service helps you orchestrate AI agents without managing the underlying resources. Importantly, everything you do in Azure AI Foundry is integrated with your coding workspaces. In the code experience, you can take advantage of multiple templates as well as a cloud hosted pre-configured dev environment to get started. And importantly, integration with GitHub, your code in Visual Studio, and even Copilot Studio for your low-code apps where you can connect to Azure AI services and more. This means that your work in Azure AI Foundry carries on seamlessly into your code and agents, or you can do everything from your code. By using a single API and calling Azure AI Foundry capabilities as service endpoints when you create projects leveraging the Azure AI Foundry SDK.
\n-For example, you can connect to different models using the new Azure AI model inference endpoint, which lets you easily compare models without changing your underlying code. And as you create your agent, you can easily assess and improve the experience. In fact, Azure AI Foundry offers a range of capabilities to help you as you continuously iterate for centralized observability, such as application tracing for debugging and performance checks, along with detailed views for execution flows integrated with your application insight resources. Additionally, automated evaluations help you continuously assess the quality of AI outputs based on key metrics, like relevance to look at how well the model meets expectations, groundedness to see how well the model refers to your grounding data, fluency for the language proficiency of the answers, and more.
\n-From there, you will use this information to create reporting, set up alerts, and share dashboards with other stakeholders. You can also take advantage of built-in safety and security controls of text, image, and multimodal content that go beyond basic system prompt guardrails to automatically detect and optionally block unwanted inputs and outputs for content involving violence, hate, sexual, and self-harm topics. Azure AI Foundry services also can help you onboard more advanced techniques as you optimize the output of your AI applications. For example, built-in services like model fine-tuning lets you adapt model output with specific training data sets that you define, helping you improve model accuracy and effectiveness in real-world applications. Additionally, integrations with Semantic Kernel and AutoGen as well as LangChain let you orchestrate execution flows for multi-agent processes, making it easier to embed AI into new or existing workflows.
\n-Then, as your apps move into production, we give you tools to monitor and manage resource utilization. Integration with Azure Monitor and Application Insights helps you quickly observe trends and get alerts for key generative AI metrics. And the Centralized Management Center helps simplify ongoing resource management and governance tasks, like managing quota, accessing permissions, and connected resources.
\n-Additionally, built-in integration across Microsoft’s security and governance stack enables you to enforce organizational standards and compliance with Azure policy, manage identity base access data and services with Microsoft Entra, leverage your data security and compliance from Microsoft Purview, and protect your AI apps at scale with ongoing threat detection and security posture management using Microsoft Defender.
\n-So, with our tour complete, next, let me show you how you can create a multi-agent application using Azure AI Foundry along with Semantic Kernel for orchestration. I’ll start by explaining the agentic app scenario, which should sound familiar if you’ve ever written a report. It’s a four-agent solution that can be initiated with any topic. There is a researcher agent that gathers information from the internet as my defined knowledge source. This process loops with the writer agent, which uses the information provided or requests more until it is satisfied. The writer agent then creates the report and loops with the editor agent, which can request additional edits until it is satisfied. And once it has approved the reported text, it shares the output with the sender agent, which emails the report using Outlook in Microsoft 365. These multi-agentic scenarios are similar in concept to microservices and other modular architectures. There are several benefits to breaking down a monolithic process, but now it’s got a new name.
\n-So let’s build it. I’ll begin in Azure AI Foundry, and in the Agents page I can see the agents that I’ve already started building, like the writer and the editor agents. The researcher and the sender agents are missing because we’re going to build them right now. I’ll start with the research agent as a new agent. Next, the setup pane on the right gives me my agent configuration options. I’ll give it a name, Research agent, and then under Deployment I can choose the model I want this agent to use. I’ll pick gpt-4o.
\n-Next, I’ll provide it with instructions for what it is supposed to do. Since it is the research agent, I’ll instruct it to use Bing search to find information. And because the research agent is part of this four-agent team, I’ll specify that it should not try to write the report, which is the job of the writer agent. It should just provide the data. Next, I’ll add a knowledge source. Again, we want Bing to ground the agent with public information from the web. Then I just need to select an Azure connection, and once I hit Connect, our agent is done. To try it out, I’ll use the playground. I’ll ask “What is dot net,” and it will generate a summarized result using knowledge from Bing search. And because it is the research agent and not the writer, you will see that its results are super concise but dense with knowledge about the topic.
\n-Next, I could specify actions, but I don’t need to. This agent already has everything it needs. And so our research agent is ready to go, and I can move on to creating our sender agent, which by the way is going to need some defined actions. To create our email sender agent, I’m going to switch over to VS Code and use the SDK. I have this Python file open, and if you look at the very bottom of the screen, there is a create_agent command. And just like we saw in the Azure AI Foundry portal, I can point it to the model for its deployment, define its name, and add its instructions, as well as its tools. As the email sender agent, we’ll provide it with Outlook as a tool, and when I run this file, it will create an agent inside of Azure AI Foundry.
\n-In fact, if I move back to our list of agents in the portal, we can see that our sender agent was just created. And so now with all of my four agents created, it’s time to wire them up using Semantic Kernel. Back in VS Code, I’ll open my program file where I’ve already started using Semantic Kernel to describe its broader process, and you will see all the logic for how each agent interacts with each other. As I explained in the graphic, each agent needs to satisfy the requirement for it’s task before moving to the next step in the process.
\n-Now, you might be wondering how to connect the orchestration logic together with my four agents. Well, let me show you. Each agent has its own configuration file for our Semantic Kernel orchestration. I’ll connect the researcher agent config with the agent ID from Azure AI Foundry. So, if I go back to the Azure AI Foundry portal and select the researcher agent, I can just copy the agent ID and go back to add it to my code, and you would do this process for each of the agents. Now with everything connected and complete, let’s try it out. Back in my code, I’ll go ahead and run to see how well our agents work together. My program asks, “What would you like a report on?” Let’s make it python, but not the code, the snake. While these agents work, I can watch exactly what they’re doing and comment on them play by play.
\n-First, we can see the researcher agent pulling some material from the web. The writer agent can then pick things up. But wait, the writer agent isn’t easily satisfied and needs some additional research on user sentiment. The researcher agent comes back with that detail, but the writer agent still has questions and needs more additional facts about the pet trade and other topics. The researcher agent, unfazed, comes back with that information. And once the writer is satisfied, it starts generating the report. When it’s finished, it sends the report to the editor agent, and it looks like the writer agent met all of the requirements, which is confirmed and approved by the editor agent, and the approval triggers the sender agent to send it out as an email. In fact, if I move over to Outlook, we can see the Report on Python Snakes just landed on my inbox, and that was just one example of how you can create agentic solutions to automate business processes.
\n-Azure AI Foundry helps you create powerful agents quickly and efficiently by providing a unified platform with extensive capabilities throughout the entire AI development lifecycle. To get started, just head over to ai.azure.com. Subscribe to Microsoft Mechanics if you haven’t already, and thank you for watching.
\n","body@stringLength":"22023","rawBody":"
Create agentic solutions quickly and efficiently with Azure AI Foundry. Choose the right models, ground your agents with knowledge, and seamlessly integrate AI into your development workflow — from early experimentation to production. Test, optimize, and deploy with built-in evaluation and management tools. See how to leverage the Azure AI Foundry SDK to code and orchestrate intelligent agents, monitor performance with tracing and assessments, and streamline DevOps with production-ready management.
\nYina Arenas, from the Azure AI Foundry team, shares its extensive capabilities as a unified platform that supports you throughout the entire AI development lifecycle.
\nThe model catalog in Azure AI Foundry gives you access to thousands of AI models, including top-tier LLMs & specialized models, with optimizations for cloud & edge deployment. Take a look.
\nWork seamlessly with Azure AI Foundry inside VS Code, GitHub, and Copilot Studio. See how to integrate AI into your dev workflow.
\nAutomate tasks like research, writing, editing, and communication using one system. Get started with Azure AI Foundry.
\n00:00 — Create agentic solutions with Azure AI Foundry
\n00:20 — Model catalog in Azure AI Foundry
\n02:15 — Experiment in the Azure AI Foundry playground
\n03:10 — Create and customize agents
\n04:13 — Assess and improve agents
\n05:58 — Monitor and manage apps
\n06:50 — Create a multi-agentic app in code
\n09:26 — Create a Sender agent
\n10:39 — How to connect orchestration logic
\n11:25 — Watch agents work
\n12:26 — Wrap up
\nGet started with Azure AI Foundry at https://ai.azure.com
\nAs Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.
\n-If you’re looking to create agentic solutions and want to move quickly and efficiently, Azure AI Foundry is the one place for discovering and accessing the right building blocks for your agents, with everything you need for AI development. Today, I’ll share the essentials for Azure AI Foundry, starting with a tour of its extensive capabilities as a unified platform that supports you throughout the entire AI development lifecycle; from initial concept with early experimentation, coding in your preferred ID, pre-production assessment, management in production, and beyond, followed by a real example of the steps for creating a multi-agent application using the new Azure AI Agents service in Azure AI Foundry, all integrated with your code. Starting with our tour, you can easily reach to Azure AI Foundry at ai.azure.com, and once you’ve created a project, panning down the left rail, you can quickly see the core experiences.
\n-First, the model catalog helps you discover and access a growing collection of thousands of models to power your individual agents as you build your system, including premium large language models from OpenAI, Meta, DeepSeek, Cohere, and more, as well as small language models like Microsoft Phi. And of course, there are hundreds of open models like those from Hugging Face for you to try out. Models are also available by area of specialization.
\n-For example, there are regional and focused models to support interactions with different spoken languages, like Mistral for European languages and Jais for Arabic. And separately, there are industry-specific models that you can choose from. The entire model catalog is hosted on Microsoft’s supercomputer infrastructure in Azure for optimized cost performance. Next, in terms of model deployment, you can choose to run models on hosted hardware with managed compute, and for those of our popular premium models, you can use our serverless API option. As you use Azure AI Foundry, you can of course also bring your own models to run on your Azure infrastructure.
\n-Then, to help you choose the right model for your agent, you can easily experiment in our playground. For agents, you can add knowledge to ground your model. You can choose files to upload, use existing search index, or add web knowledge using Microsoft Bing. There are also options to add data from Microsoft Fabric as well as SharePoint to connect with data in Microsoft 365. You can also define actions for your agents to perform, like calling APIs, functions, or using Code interpreter to write and run Python code to automate processes. And back on the homepage, clicking into AI Services provide additional task-specific capabilities that you can use to augment your agents, like speech, translation, vision, and content safety. So right from the start on your application design, you can leverage Azure AI Foundry to evaluate AI models and services for your application.
\n-Next, to create and customize agents, the new Azure AI Agents service helps you orchestrate AI agents without managing the underlying resources. Importantly, everything you do in Azure AI Foundry is integrated with your coding workspaces. In the code experience, you can take advantage of multiple templates as well as a cloud hosted pre-configured dev environment to get started. And importantly, integration with GitHub, your code in Visual Studio, and even Copilot Studio for your low-code apps where you can connect to Azure AI services and more. This means that your work in Azure AI Foundry carries on seamlessly into your code and agents, or you can do everything from your code. By using a single API and calling Azure AI Foundry capabilities as service endpoints when you create projects leveraging the Azure AI Foundry SDK.
\n-For example, you can connect to different models using the new Azure AI model inference endpoint, which lets you easily compare models without changing your underlying code. And as you create your agent, you can easily assess and improve the experience. In fact, Azure AI Foundry offers a range of capabilities to help you as you continuously iterate for centralized observability, such as application tracing for debugging and performance checks, along with detailed views for execution flows integrated with your application insight resources. Additionally, automated evaluations help you continuously assess the quality of AI outputs based on key metrics, like relevance to look at how well the model meets expectations, groundedness to see how well the model refers to your grounding data, fluency for the language proficiency of the answers, and more.
\n-From there, you will use this information to create reporting, set up alerts, and share dashboards with other stakeholders. You can also take advantage of built-in safety and security controls of text, image, and multimodal content that go beyond basic system prompt guardrails to automatically detect and optionally block unwanted inputs and outputs for content involving violence, hate, sexual, and self-harm topics. Azure AI Foundry services also can help you onboard more advanced techniques as you optimize the output of your AI applications. For example, built-in services like model fine-tuning lets you adapt model output with specific training data sets that you define, helping you improve model accuracy and effectiveness in real-world applications. Additionally, integrations with Semantic Kernel and AutoGen as well as LangChain let you orchestrate execution flows for multi-agent processes, making it easier to embed AI into new or existing workflows.
\n-Then, as your apps move into production, we give you tools to monitor and manage resource utilization. Integration with Azure Monitor and Application Insights helps you quickly observe trends and get alerts for key generative AI metrics. And the Centralized Management Center helps simplify ongoing resource management and governance tasks, like managing quota, accessing permissions, and connected resources.
\n-Additionally, built-in integration across Microsoft’s security and governance stack enables you to enforce organizational standards and compliance with Azure policy, manage identity base access data and services with Microsoft Entra, leverage your data security and compliance from Microsoft Purview, and protect your AI apps at scale with ongoing threat detection and security posture management using Microsoft Defender.
\n-So, with our tour complete, next, let me show you how you can create a multi-agent application using Azure AI Foundry along with Semantic Kernel for orchestration. I’ll start by explaining the agentic app scenario, which should sound familiar if you’ve ever written a report. It’s a four-agent solution that can be initiated with any topic. There is a researcher agent that gathers information from the internet as my defined knowledge source. This process loops with the writer agent, which uses the information provided or requests more until it is satisfied. The writer agent then creates the report and loops with the editor agent, which can request additional edits until it is satisfied. And once it has approved the reported text, it shares the output with the sender agent, which emails the report using Outlook in Microsoft 365. These multi-agentic scenarios are similar in concept to microservices and other modular architectures. There are several benefits to breaking down a monolithic process, but now it’s got a new name.
\n-So let’s build it. I’ll begin in Azure AI Foundry, and in the Agents page I can see the agents that I’ve already started building, like the writer and the editor agents. The researcher and the sender agents are missing because we’re going to build them right now. I’ll start with the research agent as a new agent. Next, the setup pane on the right gives me my agent configuration options. I’ll give it a name, Research agent, and then under Deployment I can choose the model I want this agent to use. I’ll pick gpt-4o.
\n-Next, I’ll provide it with instructions for what it is supposed to do. Since it is the research agent, I’ll instruct it to use Bing search to find information. And because the research agent is part of this four-agent team, I’ll specify that it should not try to write the report, which is the job of the writer agent. It should just provide the data. Next, I’ll add a knowledge source. Again, we want Bing to ground the agent with public information from the web. Then I just need to select an Azure connection, and once I hit Connect, our agent is done. To try it out, I’ll use the playground. I’ll ask “What is dot net,” and it will generate a summarized result using knowledge from Bing search. And because it is the research agent and not the writer, you will see that its results are super concise but dense with knowledge about the topic.
\n-Next, I could specify actions, but I don’t need to. This agent already has everything it needs. And so our research agent is ready to go, and I can move on to creating our sender agent, which by the way is going to need some defined actions. To create our email sender agent, I’m going to switch over to VS Code and use the SDK. I have this Python file open, and if you look at the very bottom of the screen, there is a create_agent command. And just like we saw in the Azure AI Foundry portal, I can point it to the model for its deployment, define its name, and add its instructions, as well as its tools. As the email sender agent, we’ll provide it with Outlook as a tool, and when I run this file, it will create an agent inside of Azure AI Foundry.
\n-In fact, if I move back to our list of agents in the portal, we can see that our sender agent was just created. And so now with all of my four agents created, it’s time to wire them up using Semantic Kernel. Back in VS Code, I’ll open my program file where I’ve already started using Semantic Kernel to describe its broader process, and you will see all the logic for how each agent interacts with each other. As I explained in the graphic, each agent needs to satisfy the requirement for it’s task before moving to the next step in the process.
\n-Now, you might be wondering how to connect the orchestration logic together with my four agents. Well, let me show you. Each agent has its own configuration file for our Semantic Kernel orchestration. I’ll connect the researcher agent config with the agent ID from Azure AI Foundry. So, if I go back to the Azure AI Foundry portal and select the researcher agent, I can just copy the agent ID and go back to add it to my code, and you would do this process for each of the agents. Now with everything connected and complete, let’s try it out. Back in my code, I’ll go ahead and run to see how well our agents work together. My program asks, “What would you like a report on?” Let’s make it python, but not the code, the snake. While these agents work, I can watch exactly what they’re doing and comment on them play by play.
\n-First, we can see the researcher agent pulling some material from the web. The writer agent can then pick things up. But wait, the writer agent isn’t easily satisfied and needs some additional research on user sentiment. The researcher agent comes back with that detail, but the writer agent still has questions and needs more additional facts about the pet trade and other topics. The researcher agent, unfazed, comes back with that information. And once the writer is satisfied, it starts generating the report. When it’s finished, it sends the report to the editor agent, and it looks like the writer agent met all of the requirements, which is confirmed and approved by the editor agent, and the approval triggers the sender agent to send it out as an email. In fact, if I move over to Outlook, we can see the Report on Python Snakes just landed on my inbox, and that was just one example of how you can create agentic solutions to automate business processes.
\n-Azure AI Foundry helps you create powerful agents quickly and efficiently by providing a unified platform with extensive capabilities throughout the entire AI development lifecycle. To get started, just head over to ai.azure.com. Subscribe to Microsoft Mechanics if you haven’t already, and thank you for watching.
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