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. Explain a bit more How to structure your answer (60–90 seconds) Define Concepts…
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. Explain a bit more How to structure your answer (60–90 seconds) Define LLMs in p…
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. Explain a bit more How to structure your answer (60–90 seconds) Define RAG in pl…
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. Explain a bit more How to structure your answer (60–90 seconds) Define Ethics in…
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. Explain a bit more How to structure your answer (60–90 seconds) Define Productio…
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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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