Our unified, interoperable AI platform enables developers to build faster and smarter, while organizations gain fleetwide security and governance in a unified portal.
Yina Arenas, Microsoft Foundry CVP, shares how to keep your development and operations teams coordinated, ensuring productivity, governance, and visibility across all your AI projects.
Learn more in this Microsoft Mechanics demo, and start building with Microsoft Foundry at ai.azure.com
Feed your agents multiple trusted data sources.
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For accurate, contextual responses, get started with Microsoft Foundry. Start here.
Apply safety & security guardrails.
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Ensure responsible AI behavior. Check it out.
Keep your AI apps running smoothly.
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Deploy agents to Teams and Copilot Chat, then monitor performance and costs in Microsoft Foundry. See how it works.
QUICK LINKS:
00:54 — Tour the Microsoft Foundry portal
03:32 — The Build tab and Workflows
05:03 — How to build an agentic app
07:02 — Evaluate agent performance
08:37 — Safety and security
09:18 — Publish your agentic app
09:41 — Post deployment
11:36 — Wrap up
Link References
Visit https://ai.azure.com and get started today
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Video Transcript:
-If you are building AI apps and agents and want to move faster with more control, the newly expounded Foundry helps you do exactly that, while integrating directly with your code. It works like a unified AI app and agent factory, with rich tooling and observability. A simple developer experience helps you and your team find the right components you need to start building your agents and move seamlessly from idea all the way to production. It is augmented by powerful new capabilities, such as an agent framework for multi-agentic apps and workflow automation, or multisource knowledge-based creation to support deep reasoning. New levels of observability across your fleet of agents then help you evaluate how well they’re operating. And it is easier than ever to ensure security and safety controls are in place to support the right level of trust and much more.
-Let’s tour the new Microsoft Foundry portal while we build an agentic app. We’ll play the role of a clothing company using AI to research new market opportunities. The homepage at ai.azure.com guides you right through a build experience. It’s simple to start building, to create an agent, design a workflow, and browse available AI models right from here. Alternatively, you can quickly copy the project endpoint, the key, and the region to use it directly in your code with the Microsoft Foundry SDK. One of the most notable improvements is how everything you need to do is aligned to the development lifecycle.
-If you are just getting started, the Discovery tab makes it simple to find everything you need. Feature models are front and center, from OpenAI, Grok, Meta, DeepSeek, Mistral AI, and now for the first time, Anthropic. You can also browse model collections, including models that you can run from your local device from Foundry Local. Model Leaderboard then helps you reference how the top models compare across quality, safety, throughput, and cost. And you’ll see the feature tools, including MCP servers, that you can connect to. Then moving to the left nav, in Agents, you can find samples for different standalone agent types to quickly get you up and running.
-In Models, you can browse a massive industry-leading catalog of thousand of foundational open source and specialized models. Click any model to see its capabilities, like this one for GPT-5 Chat. Then clicking into Deploy, we can try it out from here. I’ll add a prompt: “What is a must-have apparel for the fall in the Pacific Northwest?” Now, looking at its generated response with recommendations for outerwear, it looks like GPT-5 Chat knows that it rains quite a bit here. If I move back to the catalog view, we can also see the new model router that automatically routes prompts to the most efficient models in real time, ensuring high-quality results while minimizing costs. I already have it deployed here and ready to use.
-Under Tools, you’ll find all of the available tools that you can use to connect your agents and apps. You can easily find MCP servers and more than a thousand connectors to add to your workflows. You can add them from here or right as you’re building your agent. Next, to accelerate your efforts, you can access dozens of curated solution templates with step-by-step instructions for coding AI right into your apps. These are customizable code samples with preintegrated Azure services and GitHub-hosted quickstart guides for different app types. So there are plenty of components to discover while designing your agent.
-Next, the Build tab brings powerful new capabilities, whether you’re creating a single agent or a multi-agentic solution. Build is where you manage the assets you own: agents, workflows, models, tools, knowledge and more. And straightaway it’s easy to get to all your current agents or create new ones. I have a few here already that I’ll be calling later to support our multi-agentic app, including this research agent. In Workflows, you can create and see all your multi-agentic apps and workflow automations.
-To get started, you can pick from different topologies such as Sequential, Human in the Loop, or Group Chat and more. I have a few here, including this one for research that we’ll use in our agentic app. We’ll go deeper on this in just a moment. As you continue building your app, your deployed models can be viewed in context. Here’s the model router that we saw before. And then further down the left rail you’ll find fine-tuning options where you can customize model behavior and outputs using supervised learning, direct preference optimization, and reinforcement techniques. Under the Tools, it’s easy to see which ones are already connected to your environment. Knowledge then allows you to add knowledge bases from Foundry IQ so you can bring not just one but multiple sources, including SharePoint online, OneLake, which is part of Microsoft Fabric, and your search index to ground your agents.
-And in Data, you can create synthetic datasets, which are very handy for fine-tuning and evaluation. Now that we have the foundational ingredients for our agentic app collected, let’s actually build it. I’ll start with a multi-agent workflow that my team is working on. Workflows are also a type of agent with similar constructs for development, deployment, and the management, and they can contain their own logic as well as other agents. The visualizer lets you easily define and view the nodes in the workflow, as well as all connected agents. You can apply conditions like this to a workflow step. Here we’re assessing the competitiveness of the insights generated as we research opportunities for market expansion.
-There is also a go-to loop. If the insights are not competitive, we’ll iterate on this step. For many of these connectors, you can add agents. I’m going to add an existing agent after the procurement researcher. I’ll choose an agent that we’ve already started working on, the research agent, and jump into the editor. Note that the Playground tab is the starting point for all agents that you create. You can choose the model you want. I’ll choose GPT-5 Chat and then provide the agent with instructions. I’ll add mine here with high-level details for what the agent should do. Below that, in Tools, you can see that my research agent is already connected to our internal SharePoint site in Microsoft 365. I can also add knowledge bases to ground responses right from here. I can turn on memory for my agent to retain notable context and apply guardrails for safety and security controls. I’ll show you more on that later. Agents are also multimodel, including voice, which is great for mobile apps. Using voice, I’ll prompt it with: “What industry is Zava Corp in, and what goods does it produce?”
-[AI] Zava Corporation operates in the apparel industry. It focuses on producing a wide range of clothing and fashion-related goods.
-Next, I’ll type in a text prompt, and that will retrieve content from our SharePoint site to generate its response. And importantly, as I make these changes to my agent, it will now automatically version them, and I can always revert to a previous version. Then as the build phase continues, it’s easy to evaluate agent performance.
-In Evaluations, I can see all my agent runs. I’ve already started creating an evaluation for our agent using synthetic data to check that we are hitting our goals for output quality and safety. From the Agent, we can review its runs and traces to diagnose latency bottlenecks. And under the Evaluation tab, you can see that our AI quality and safety scores could be better. Using these insights, let’s update our agent and make improvements. Everything shown in the web portal can also be done with code. So let’s do this update in VS Code. This is the same multi-agentic workflow I showed you before, with all of its logic now represented in code. The folders on the left rail represent our different agents, and the workflow structure describes the multi-agent reasoning process. It’s designed to take incoming requests and route them to the relevant expert agent to complete the tasks. We have an intent classifier agent, a procurement researcher, the market researcher one that we just built, and two more with expertise in negotiation and review.
-And the workflow is connected to a knowledge base with multiple sources to inform agentic responses. This includes a search index for supplier information, relevant financial data from Microsoft Fabric, product data from SharePoint, and we can connect to available MCP servers like this one from GitHub. Having this rich multisource knowledge base feeding our agentic workflow should ensure more accurate results. In fact, if we look at the evaluation for this workflow, you will see that AI quality is a lot higher overall. But we still have to do some work on safety. We’ll address this by adding the right safety and security controls right from Microsoft Foundry. For that, we’ll head over to Guardrails where you can apply controls based on specific AI risks.
-I’ll target jailbreak attack, and then I can apply additional associated controls like content safety and protected materials to ensure our agents also behave responsibly. And I can scope what this guardrail should govern: either a model or an agent; or in my case, I’ll select our workflow to address the low safety score that we saw earlier. And with that, it’s ready to publish. In fact, we’ve made it easier to get your apps and agents into the productivity tools that people use every day. I can publish our agentic app directly into Microsoft Teams and Copilot Chat right from our workflow. And once it is approved by the Microsoft 365 admin, business users can find it in the Agent Store and pin it for easy access. Now, with everything in production, your developer and operation teams can continue working together in Microsoft Foundry, post-deployment and beyond.
-The Operate tab has the full Foundry control plane. In the overview, you can quickly monitor key operational metrics and spot what needs your attention. This is a full cross-fleet view of your agents. You can also filter by subscription and then by project if you want. The top active alerts are listed right here for me to take action. And I can optionally view all alerts if I want, along with rollout metrics for estimated cost, agent success rates, and total token usage. Below that, we can see the details of agent runs of our time, along with top- and bottom-performing agents with trends for each. All performance data is built on open telemetry standards that can be easily surfaced inside Azure Monitor or your favorite reporting tool.
-Next, under Assets, for every agent, model, and tool in your environment, you can see metrics like status, error rates, estimated cost, token usage, and number of runs. This gives you a quick pulse on performance activity and health for each asset. And you can click in for more details if you want to. Compliance then lets IT teams view and set default policies by AI risk for any asset created. You can add controls and then scope it by the entire subscription or resource group. That way they will automatically inherit governance controls. Under Quota, you can keep all of your costs in check while ensuring that your AI applications and agents stay within your token limits. And finally, under Admin, you can find all of your resources and related configuration controls for each project in one place, and click in to manage roles and access. If you go back, the newly integrated AI gateways also allow you to connect and manage agents, even from other clouds.
-So that’s how the expanded Microsoft Foundry simplifies the development and operations experience to help you and your team build powerful AI apps and agents faster, with more control, while integrated directly into your code. Visit ai.azure.com to learn more and get started today. Keep watching Microsoft Mechanics for the latest tech updates, and subscribe if you haven’t already. Thanks for watching.
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