AI Agents
36 guides on AI Agents.
An agent is a loop. The model asks for a tool, your code runs it, the result goes back, and it decides again. These guides cover what that loop costs per turn, where it fails quietly, and the controls worth putting around it before it can touch anything that matters.
Securing a Remote MCP Server in C#: OAuth 2.1 and Protected Resource Metadata
The moment an MCP server leaves localhost it becomes a public API with tool-calling attached. Here is what the spec actually requires, what the C# SDK gives you, and the parts you still have to think about.
Agentic RAG in .NET: When One Retrieval Is Not Enough
Classic RAG runs one search and hopes. Agentic RAG lets the model decide what to look up, how often, and when it has enough — here is what that costs and how to build it in C#.
Why Your .NET AI Agent Feels Slow (And What to Fix First)
Perceived latency in an agent is rarely the model. A latency budget for .NET agents — where the seconds actually go, measured, and the fixes ordered by what they return.
CodeAct in the Microsoft Agent Framework: What .NET Developers Need to Know
CodeAct collapses the tool-calling loop into one generated program and reports large latency and token savings — but it is Python-only today. What it is, why it works, and what a .NET team can use right now.
Model Routing and Fallback in .NET: One Interface, Several Models
Route each request to the cheapest model that can handle it, and fail over when a provider degrades — built on Microsoft.Extensions.AI with C# you can drop into an existing app.
Prompt Injection in .NET Agents: What Actually Works as a Defence
Prompt injection is not a prompt problem, so prompt fixes do not solve it. A .NET engineer's guide to the defences that hold — capability scoping, provenance tracking, output gating — with C# you can lift.
30 .NET AI & Agent Interview Questions (With Answers That Actually Land)
The AI questions .NET developers are being asked in 2026 — fundamentals, Microsoft.Extensions.AI, the Agent Framework, RAG, tools and production concerns — with model answers and what each one is really testing.
Run Your Whole AI Agent Stack Locally with Aspire (Ollama + Qdrant + Traces)
Wire an LLM, a vector database and your API into one F5 with Aspire — no docker-compose, no API keys during development, and OpenTelemetry traces of every model call for free.
RAG vs Fine-Tuning: How to Actually Decide (A .NET Engineer's Guide)
RAG or fine-tuning? A decision framework based on what your problem actually is — knowledge, behaviour, format or cost — plus what each one really costs to run on .NET.
Text-to-SQL in .NET: Letting Users Query a Database in Plain English (Safely)
Build a natural-language-to-SQL feature in C# — schema grounding, a read-only execution boundary, statement validation and the failure modes that decide whether it survives production.
The A2A (Agent2Agent) Protocol for .NET Developers
What the A2A (Agent2Agent) protocol is, how it differs from MCP, and how .NET developers build A2A agents in C# with the A2A SDK and Microsoft Agent Framework.
The Agent Harness in Microsoft Agent Framework (.NET Guide)
The Agent Harness in Microsoft Agent Framework gives .NET agents sandboxed shell execution, file access, and human-in-the-loop approvals. How to wire it up.
Agent Skills for .NET: A Practical Guide
Agent Skills for .NET is now stable in the Microsoft Agent Framework. Learn how to package, distribute, and govern agent skills in C#, with working code.
Foundry IQ and Agent Memory: Managed RAG and Memory for .NET Agents
Foundry IQ gives .NET agents serverless retrieval, knowledge bases, and managed memory. What it replaces in your RAG stack, and when to keep pgvector.
Hosted Agents in Foundry Agent Service: A .NET Developer's Guide
Hosted agents in Foundry Agent Service are GA: per-session sandboxes, scale-to-zero, and a simple path to deploy Microsoft Agent Framework agents from .NET.
What BUILD 2026 Means for .NET Agent Developers
Build 2026 for .NET agent developers: Agent Harness, Hosted Agents, Agent Skills, CodeAct, and Foundry IQ memory — what shipped, why it matters, what to do.
Caching LLM Responses in .NET
LLM calls are slow and cost money. Add caching to your .NET AI app — response caching and prompt caching — to cut latency and spend without losing quality.
Content Moderation and Guardrails for .NET AI Apps
User input and AI output both need checking. How to add content moderation and guardrails to a .NET AI app — filtering unsafe input, output, and enforcing scope.
Embeddings in .NET, Explained
What are embeddings, and how do you generate and use them in C#? A clear, practical explanation of vector embeddings for .NET developers — with code and use cases.
Prompt Engineering for .NET Developers
Prompt engineering, minus the hype. Practical techniques .NET developers can use to get reliable, consistent output from an LLM — with C# examples.
Resilient LLM Calls in .NET with Polly
LLM APIs rate-limit, time out, and hiccup. Make your .NET AI calls resilient with Polly — retries with backoff, circuit breakers, and timeouts done right.
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.
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.
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.
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#.