Agentic .NET
Building AI agents with .NET & the cloud. In-depth, code-first guides from production experience.
Build Your First Tool-Using AI Agent in C#
A step-by-step tutorial: build an AI agent in C# with the Microsoft Agent Framework that calls your own tools, remembers the conversation, and chains multiple tool calls.
Choosing a Model for Your .NET Agent
GPT-class, small, or open-weight? A practical framework for picking the right LLM for a .NET agent, balancing capability, latency, cost, and where the model runs.
Cost Control for Production AI Agents in .NET
AI agent bills scale with usage and can surprise you. Practical techniques for controlling token spend in .NET agents: right-sizing, caching, context trimming, and budgets.
Deploy a .NET AI Agent to Azure Container Apps
Take a .NET AI agent from your laptop to production on Azure Container Apps: containerize it, wire up secrets with managed identity, deploy, and scale to zero when idle.
Running a .NET AI Agent on Google Kubernetes Engine (GKE)
Deploy a .NET AI agent to GKE the production way: containerize, authenticate with Workload Identity instead of keys, expose it through an ingress, and autoscale on load.
Function Calling vs MCP: When to Use Which in .NET
Should a tool be a C# method or an MCP server? A clear decision guide for .NET agents on when to use built-in function calling versus the Model Context Protocol.
Multi-Agent Orchestration in .NET: Graph Workflows Explained
Coordinating multiple AI agents by prompt alone is fragile. Here is how the Microsoft Agent Framework's graph-based workflows make multi-agent systems in .NET explicit, testable, and observable.
Observability for AI Agents with OpenTelemetry in .NET
When an agent misbehaves, logs are not enough. How to instrument a .NET agent with OpenTelemetry so you can see every tool call, token, and decision it made.
Giving Your .NET Agent a Knowledge Base: RAG in C#
Your agent does not know your documents — until you add retrieval. A practical guide to RAG in .NET: embeddings, a vector store, and grounding an agent in your own data.
Securing AI Agents in .NET: Auth, Secrets, and Guardrails
An AI agent that can call tools is a new attack surface. A practical security checklist for .NET agents: authenticating callers, protecting secrets, and guardrailing tool use.
Semantic Kernel vs the Microsoft Agent Framework: Which Should You Use in 2026?
Semantic Kernel or the Microsoft Agent Framework? Since the 1.0 GA merge, the answer changed. A clear breakdown of what to use for new projects and how to migrate existing ones.
Streaming AI Agent Responses to a Web UI in .NET
Waiting for a full agent response feels broken. Learn how to stream tokens from a .NET agent to the browser with Server-Sent Events, so answers appear word-by-word.
Testing and Evaluating AI Agents in .NET
AI agents are non-deterministic, but that does not make them untestable. A practical strategy for unit-testing tools, mocking the model, and evaluating agent quality in .NET.
The Microsoft Agent Framework for .NET: A Developer's Guide
Microsoft Agent Framework 1.0 unifies Semantic Kernel and AutoGen into one .NET SDK for building AI agents. Here is what it is, why it exists, and how to build your first agent in C#.
Model Context Protocol for .NET Developers: Giving Your Agent Real Tools
A practical guide to Model Context Protocol (MCP) for .NET developers: connect a Microsoft Agent Framework agent to an MCP server and expose your own agent as one.