Tutorials ASP.NET Core with Agentic AI Tutorial

AI Orchestration with Kernel — Complete Guide

AI Orchestration with Kernel — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ASP.NET Core with Agentic AI Tutorial on Toolliyo Academy.

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ASP.NET Core with Agentic AI Tutorial · Lesson 27 of 100

AI Orchestration with Kernel

AI basicsAgents

AI basics · 1 — Setup · ~6 min · Module 3: Semantic Kernel

What is this?

Kernel orchestration combines prompts, plugins, planners, and memory in one execution graph. AgentNest orchestrators invoke kernel processes for multi-step CRM playbooks.

Why should you care?

Kernel already knows plugins — orchestration layers add tenant policy, telemetry, and human gates around InvokeAsync.

See it live — copy this example

Paste into an ASP.NET Core 8+ / AgentNest project, then run with dotnet run (set your API keys in user-secrets).

// AgentNest.Orchestration/KernelPlaybookRunner.cs
public sealed class KernelPlaybookRunner(Kernel kernel, ILogger<KernelPlaybookRunner> log)
{
    public async Task RunCrmPlaybookAsync(Guid dealId, CancellationToken ct)
    {
        var enrich = kernel.Plugins["crm"]["EnrichDeal"];
        var draft = kernel.CreateFunctionFromPrompt("Draft email for deal {{$dealId}}");
        await kernel.InvokeAsync(enrich, new() { ["dealId"] = dealId.ToString() }, cancellationToken: ct);
        var email = await kernel.InvokeAsync(draft, new() { ["dealId"] = dealId.ToString() }, cancellationToken: ct);
        log.LogInformation("Playbook complete for {DealId}", dealId);
    }
}

What happened?

  • KernelPlaybookRunner chains a plugin function and a prompt function sequentially with structured logging.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Register kernel with crm plugin in DI.
  2. Define playbooks as explicit runner methods for regulated flows.
  3. Pass CancellationToken into every InvokeAsync.
  4. Persist playbook state after enrich step for crash recovery.
  5. Return structured PlaybookResult DTO instead of void.

Remember

Orchestrate SK with explicit playbook runners for CRM/ERP. Chain InvokeAsync calls with logging and cancellation. Reserve planners for exploratory analytics only.

CRM playbook button

SDR clicks Enrich + Draft on a stalled opportunity.

Outcome: KernelPlaybookRunner runs two kernel steps with full telemetry in one API call.

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 ASP.NET Core with Agentic AI.
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 ASP.NET Core with Agentic AI?
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 ASP.NET Core with Agentic AI 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…
Junior Detailed
Describe a real-world scenario where Production mattered in a ASP.NET Core with Agentic AI 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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ASP.NET Core with Agentic AI Tutorial
Course syllabus

ASP.NET Core with Agentic AI Tutorial

Module 1: AI and Agentic AI Foundations
Module 2: ASP.NET Core AI Fundamentals
Module 3: Semantic Kernel
Module 4: AI Agents and Multi-Agent Systems
Module 5: RAG and Vector Databases
Module 6: AI Security and Observability
Module 7: Cloud-Native AI and DevOps
Module 8: AI SaaS and Enterprise Systems
Module 9: AI System Design and Architecture
Module 10: Enterprise AI Projects
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