Tutorials Agentic AI with .NET Tutorial

Introduction to Semantic Kernel

Introduction to Semantic Kernel: free step-by-step lesson with examples, common mistakes, and interview tips — part of Agentic AI with .NET Tutorial on Toolliyo Academy.

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Agentic AI with .NET Tutorial · Lesson 11 of 120

Semantic Kernel

Foundations & SKTools & RAGProduct & OpsProjects

Foundations & SK · 1 — Agent basics · ~6 min · Semantic Kernel

What is this?

Semantic Kernel (SK) is a .NET SDK that connects chat models, prompts, and plugins (tools). You build a Kernel, register services, then invoke prompts or functions.

Why should you care?

For AgentVerse on .NET, SK is a practical way to keep prompts and tools organized instead of scattered HttpClient calls.

See it live — copy this example

Use .NET 8+. Run Semantic Kernel samples in a console or Web API. Keep API keys in user secrets or environment variables — never in source.

// dotnet add package Microsoft.SemanticKernel
using Microsoft.SemanticKernel;

var builder = Kernel.CreateBuilder();
// next lessons: add chat completion + plugins
var kernel = builder.Build();
Console.WriteLine($"Plugins: {kernel.Plugins.Count}");

What happened?

  • CreateBuilder configures the kernel.
  • Build() creates the runtime object.
  • Plugins hold your tools.

Practice next

  1. Create a console app.
  2. Add Microsoft.SemanticKernel.
  3. Build an empty kernel and print plugin count.
  4. Add logging to the builder.
  5. Create a second project for Web API later.

Remember

SK = kernel + prompts + plugins. Start empty, then add chat + tools. Keep config outside code.

SK as the spine

All AgentVerse skills register as plugins on one kernel per request.

Outcome: Clear place to attach tools.

Interview prep for this lesson

Practice these questions aloud after reading—each links to a full structured answer.

Junior Detailed
Explain Concepts in the context of Agentic AI with .NET.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. How to structure your answer (60–90 seconds) Define Concepts in plain language f…
Mid Detailed
What are common mistakes teams make with LLMs when using Agentic AI with .NET?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. How to structure your answer (60–90 seconds) Define LLMs in plain language for A…
Senior Detailed
How would you debug a production issue related to RAG in a Agentic AI with .NET application?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. How to structure your answer (60–90 seconds) Define RAG in plain language for Ag…
Mid Detailed
Compare two approaches to Ethics—when would you choose each?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. How to structure your answer (60–90 seconds) Define Ethics in plain language for…
Junior Detailed
Describe a real-world scenario where Production mattered in a Agentic AI with .NET project.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. How to structure your answer (60–90 seconds) Define Production in plain language…
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Agentic AI with .NET Tutorial
Course syllabus

Agentic AI with .NET Tutorial

Agentic Foundations
Semantic Kernel
.NET AI Wiring
Framework Choices
RAG & Memory
Tools & Automation
Multi-Agent Patterns
ASP.NET Core Integration
Security & Governance
Cloud & Operations
Advanced Agent Behavior
Capstone Projects
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