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Developing AI-enabled Applications with .NET
Learn to build production-minded AI-enabled applications with C# and .NET. You’ll integrate generative models through Microsoft.Extensions.AI, add function tools and agent workflows with Microsoft Agent Framework, publish secure MCP servers, ground responses with enterprise data, and evaluate quality, safety, latency, and cost. The path also introduces deployment, identity, observability, and operational controls for .NET agents in Microsoft Foundry.
**This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Agents and Interoperable Tools
Build stateful agents, extend them with secure interoperable tools, and transition existing Semantic Kernel applications when needed.
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What You'll Learn
- Build conversational and task-oriented AI features in .NET and select between direct model calls, tool-calling patterns, and agents.
- Extend and ground applications with function tools, secure MCP servers, ingestion pipelines, vector and hybrid retrieval, and source citations.
- Test and evaluate AI quality, safety, reliability, latency, and cost, and deploy and monitor .NET agents with Microsoft Foundry.
- \--- Foundations and Application Patterns --- \
- 1. Building Generative AI Applications with .NET\
- 2. Agentic AI Fundamentals in the Microsoft Ecosystem
- \
- -- Agents and Interoperable Tools --- \
- 3. Building AI Agents with .NET and Microsoft Agent Framework \
- 4. Using the C# Model Context Protocol SDK\
- 5. AI-Enabled Application Integration with Semantic Kernel
- \
- -- Grounding and Quality---\
- 6. Building Grounded AI Applications with .NET \
- 7. Testing and Evaluating AI Applications in .NET
- \
- -- Production Agent Operations ---\
- 8. Deploying and Operating .NET Agents with Foundry
- Intermediate experience with C# and .NET, including asynchronous programming, dependency injection, and application configuration. Familiarity with ASP.NET Core, HTTP/JSON APIs, and automated testing is recommended. Learners should understand basic generative AI concepts such as models, prompts, tokens, and tool calling. Azure fundamentals and access to a Microsoft Foundry project are recommended for the deployment content.
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