Tutorials ASP.NET Core with Agentic AI Tutorial

Tool Calling — Complete Guide

Tool Calling — 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 33 of 100

Tool Calling

AI basicsAgents

AI basics · 1 — Setup · ~6 min · Module 4: AI Agents and Multi-Agent Systems

What is this?

Tool calling is the model-driven pattern where the LLM emits structured tool invocations your runtime executes. AgentNest enables automatic function choice in SK agent loops for CRM and ERP queries.

Why should you care?

Users ask natural questions; tool calling maps them to GetAccount or fetch_balance without brittle keyword routing.

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.Agents/ToolCalling/CrmToolAgent.cs
public sealed class CrmToolAgent(Kernel kernel)
{
    public async Task<string> AskAsync(string question, CancellationToken ct)
    {
        var settings = new OpenAIPromptExecutionSettings { ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions };
        var chat = kernel.GetRequiredService<IChatCompletionService>();
        var result = await chat.GetChatMessageContentAsync(question, settings, kernel, cancellationToken: ct);
        return result.Content ?? "";
    }
}

What happened?

  • AutoInvokeKernelFunctions lets SK execute CrmPlugin tools when the model requests them, then continue until a final answer.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Register CRM plugins on kernel before enabling auto invoke.
  2. Set ToolCallBehavior only on trusted internal routes.
  3. Log tool name and arguments on each auto invocation.
  4. Switch to RequireApproval for tools that send email.
  5. Return tool call trace JSON to the client for debugging.

Remember

Tool calling connects NL questions to kernel functions. AutoInvokeKernelFunctions runs tools inside SK chat loops. Log and validate every tool invocation.

CRM natural language

Reps type questions instead of clicking through CRM reports.

Outcome: CrmToolAgent auto-invokes GetPipeline without custom intent classifiers.

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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