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Building AI agents with .NET and the cloud — 61 guides · page 3 of 4.
Adding Memory and State to Your .NET Agent
A stateless agent forgets everything between messages. How to give a .NET agent short-term memory with threads, persist it across sessions, and add long-term memory.
Build an AI Chatbot in Blazor with .NET
A practical guide to building an AI chatbot in Blazor: wire up a chat UI, stream the model's response token-by-token, and keep the conversation in state — all in C#.
Building Your Own MCP Server in C#
Expose your tools to any AI agent by building an MCP server in C#. A practical walkthrough of the Model Context Protocol server SDK, defining tools, and choosing a transport.
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.
How to Call the OpenAI API from C# (2026 Guide)
A step-by-step guide to calling the OpenAI API from C#: install the SDK, send your first chat completion, stream responses, and keep your API key safe in .NET.
Running a Local LLM in .NET with Ollama
Run an LLM locally and call it from C# with Ollama — no API key, no cloud, no per-token cost. How to connect .NET to a local model and when local makes sense.
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.
Structured Output from .NET Agents: Getting Reliable JSON in C#
An agent that returns prose is hard to program against. How to get reliable, strongly-typed JSON out of a .NET agent — with schemas, typed responses, and validation.
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#.