What Is Microsoft Foundry? A Hands-On Guide to Building AI Apps and Agents
Microsoft Foundry is Microsoft's platform for building, deploying, and managing generative AI applications, AI agents, and enterprise AI solutions in Azure.
It brings together AI models, agents, enterprise data, evaluation, monitoring, security, and development tools in one environment.
Microsoft Foundry gives you the tools to take an AI model and turn it into an actual application, AI agent, chatbot, or knowledge assistant.
If you've previously heard the name Azure AI Foundry, you're in the right place. Microsoft has continued evolving the platform and its branding, so you'll still encounter both terms in documentation, tutorials, and search results.
🔎 Microsoft Foundry at a Glance
What is it?
Microsoft's Azure-based platform for developing generative AI applications and AI agents.
What can you build?
AI assistants, AI agents, knowledge assistants, RAG applications, chatbots, and custom AI solutions.
Who is it for?
Developers, Azure administrators, cloud engineers, architects, data professionals, and other IT professionals.
Best way to learn it?
Actually build something with it.
What Is Microsoft Foundry?
Microsoft Foundry is an Azure-based development platform designed to help organizations create, deploy, evaluate, manage, and govern generative AI applications.
Instead of forcing developers to assemble every AI component separately, Foundry brings many of those services together into a common environment.
Using Microsoft Foundry, you can work with:
- Large language models and other AI models
- AI agents
- Model deployments
- Knowledge sources
- Retrieval and grounding
- Prompt-based AI applications
- Agent tools
- Model evaluation
- Tracing and monitoring
- Enterprise security and governance
This makes Microsoft Foundry particularly useful for organizations that want to experiment with different AI models and build production-ready AI solutions inside Azure.
Microsoft Foundry vs. Azure AI Foundry
One source of confusion for people learning the platform is the naming.
Microsoft Foundry evolved from what Microsoft previously called Azure AI Foundry. Because the platform has gone through several naming changes, you'll still find documentation, tutorials, videos, and search results that use the older Azure AI Foundry name.
If you're searching for tutorials, try both Microsoft Foundry and Azure AI Foundry. A lot of useful training content still uses the older name.
Search terms such as the following may all lead you into closely related areas of Microsoft's AI platform:
- Microsoft Foundry tutorial
- Azure AI Foundry tutorial
- Microsoft Foundry labs
- Azure AI Foundry labs
- Microsoft Foundry agents
- Microsoft Foundry training
What Can You Build with Microsoft Foundry?
Microsoft Foundry isn't limited to basic AI chatbots. It can support a wide range of generative AI solutions.
🤖 AI Agents
AI agents combine an AI model with instructions, tools, and data so they can perform more sophisticated tasks than a traditional chatbot.
Depending on how the agent is configured, it may be able to search for information, retrieve enterprise data, call tools, interact with APIs, or complete multi-step tasks.
💬 AI Assistants
You can build AI assistants that answer questions, summarize information, generate content, assist users with tasks, or provide support.
📚 Knowledge Assistants
A knowledge assistant can use your organization's own information to produce more useful and context-aware responses.
For example, you might connect an AI application to:
- Internal documentation
- Company policies
- Technical manuals
- Product documentation
- Training material
- Support articles
- Enterprise data sources
🔍 RAG Applications
Microsoft Foundry can also be used to build retrieval-augmented generation, or RAG, solutions.
RAG allows an AI application to retrieve relevant information before generating its answer. That makes it possible to ground AI responses in your own data instead of relying only on what the model learned during training.
🧪 Want to Try Microsoft Foundry Yourself?
I've created a step-by-step Microsoft Foundry lab collection where you build a working knowledge assistant using the real Microsoft Azure and Foundry interfaces.
Lab 1 is public, so you can start without an All-Access Pass.
What Are Microsoft Foundry Models?
At the center of most generative AI applications is an AI model.
Microsoft Foundry provides access to a large catalog of AI models that can be used for different workloads.
Depending on your requirements, different models may be better suited for:
- General chat
- Reasoning
- Coding
- Summarization
- Content generation
- Document processing
- AI agent workflows
- Low-latency applications
- Cost-sensitive workloads
One of the advantages of learning Microsoft Foundry is that you begin to understand how model deployment fits into the larger AI application architecture.
What Is Microsoft Foundry Agent Service?
Microsoft Foundry Agent Service provides managed infrastructure for building and deploying AI agents.
An agent typically combines several components:
1️⃣ An AI model
The model provides the reasoning and language capabilities.
2️⃣ Instructions
Instructions define how the agent should behave and what it should do.
3️⃣ Tools
Tools allow the agent to interact with external systems or perform actions.
4️⃣ Knowledge
Data sources provide information the agent can use when answering questions.
5️⃣ Application logic
Logic controls how the agent responds, retrieves information, and completes tasks.
This makes Microsoft Foundry Agent Service useful for organizations that want to move beyond simple prompt-and-response AI applications.
How Does Microsoft Foundry Work?
A typical Microsoft Foundry project follows a workflow similar to this:
1️⃣ Create your Foundry resources
Set up the Azure resources and Foundry project that will host your AI solution.
2️⃣ Choose an AI model
Select a model appropriate for your application.
3️⃣ Deploy the model
Create a model deployment that your application or agent can use.
4️⃣ Configure your AI application
Define instructions and application behavior.
5️⃣ Add knowledge and tools
Connect your application to data, search, APIs, or external tools.
6️⃣ Test and evaluate
Experiment with prompts and evaluate the quality of the responses.
7️⃣ Monitor and improve
Track the application and continue refining it as requirements change.
Reading about these steps is helpful, but actually performing them in Azure is where the platform begins to make much more sense.
Who Should Learn Microsoft Foundry?
Microsoft Foundry is not just for AI developers.
| Role | Why Microsoft Foundry Matters |
|---|---|
| Azure Administrators | Identity, permissions, Azure resources, networking, monitoring, and governance |
| Developers | Building AI applications, agents, and model integrations |
| Cloud Engineers | Deploying and operating AI workloads in Microsoft Azure |
| Solutions Architects | Designing enterprise AI architectures and integrations |
| IT Professionals | Understanding how AI fits into existing Azure infrastructure and security |
For IT professionals in particular, Foundry is interesting because enterprise AI still depends heavily on traditional infrastructure concepts.
Someone still needs to understand identity, permissions, Azure resources, networking, monitoring, governance, security, cost management, and data access.
Why Learn Microsoft Foundry?
Generative AI is rapidly becoming part of mainstream cloud architecture.
Organizations are beginning to integrate AI into applications, support systems, internal knowledge bases, automation workflows, and business processes.
If you already work with Microsoft Azure, learning Microsoft Foundry can help you understand how familiar Azure concepts connect with modern AI services.
You'll begin working with concepts such as:
- AI model deployment
- Prompt engineering
- AI agents
- Retrieval-augmented generation
- Knowledge grounding
- Enterprise AI security
- AI monitoring
- Model evaluation
The Best Way to Learn Microsoft Foundry Is Hands-On
Microsoft provides extensive documentation and learning material for Foundry, but the platform includes enough moving pieces that it can be difficult to understand how everything fits together by reading documentation alone.
That's why I recommend actually building something.
A good first project is a knowledge assistant because it exposes you to several important Microsoft Foundry concepts while producing something tangible that you can test.
As you build the solution, you'll begin to see how resources, projects, models, deployments, knowledge sources, retrieval, and agents fit together.
🚀 Build a Knowledge Assistant with Microsoft Foundry
I've created a hands-on Microsoft Foundry lab collection on labITpro that walks you through building a knowledge assistant step by step using the actual Microsoft Azure and Foundry interfaces.
Rather than simply watching someone demonstrate the platform, you perform the configuration yourself.
The first lab is public, so you can start immediately without purchasing an All-Access Pass.
If you already have a labITpro All-Access Pass, the complete Microsoft Foundry lab collection is included along with 100+ additional hands-on IT and cloud labs.
Microsoft Foundry FAQ
What is Microsoft Foundry?
Microsoft Foundry is Microsoft's Azure-based platform for building, deploying, managing, evaluating, and governing generative AI applications and AI agents.
Is Microsoft Foundry the same as Azure AI Foundry?
Microsoft Foundry is the current evolution of Microsoft's Azure AI development platform. You'll still encounter the older Azure AI Foundry name in documentation, tutorials, videos, and search results.
What is Microsoft Foundry used for?
Microsoft Foundry can be used to build AI assistants, AI agents, RAG applications, knowledge assistants, chatbots, and other generative AI solutions.
Can Microsoft Foundry build AI agents?
Yes. Microsoft Foundry includes agent capabilities designed to help developers build, deploy, and manage AI agents that can work with models, tools, and enterprise data.
What is RAG in Microsoft Foundry?
RAG stands for retrieval-augmented generation. It allows an AI application to retrieve relevant information from a knowledge source before generating a response.
Do I need Python to use Microsoft Foundry?
Not necessarily. Some Microsoft Foundry tasks can be completed directly through the Azure and Foundry interfaces. More advanced applications commonly use Python, C#, JavaScript, TypeScript, REST APIs, or SDKs.
Is Microsoft Foundry only for developers?
No. Microsoft Foundry is also relevant to Azure administrators, cloud engineers, solutions architects, data professionals, and other IT professionals who work with Azure infrastructure, security, governance, and enterprise applications.
How do I learn Microsoft Foundry?
You can learn Microsoft Foundry through Microsoft documentation and training, but hands-on practice is one of the best ways to understand how projects, models, agents, tools, knowledge sources, and Azure resources work together.
Start Learning Microsoft Foundry
Microsoft Foundry brings together many of the technologies used to build modern enterprise AI solutions, including AI models, AI agents, retrieval, knowledge grounding, evaluation, monitoring, security, and Azure governance.
If your goal is to learn Microsoft Foundry, don't stop at reading about it.
Build something.
The easiest way to understand projects, model deployments, agents, knowledge sources, and retrieval is to work through the process yourself.
Ready to Get Hands-On with Microsoft Foundry?
Build a working knowledge assistant while learning Microsoft Foundry step by step.
Lab 1 is public and available to everyone.