Tutorials ASP.NET Core with Agentic AI Tutorial

AI Agent Architecture — Complete Guide

AI Agent Architecture — 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 31 of 100

AI Agent Architecture

AI basicsAgents

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

What is this?

Agent architecture defines layers: perception, planning, tool execution, memory, and response formatting. AgentNest agents build this as explicit classes with injected dependencies.

Why should you care?

Without a shared pattern, CRM and ERP teams ship incompatible agent APIs that operations cannot monitor uniformly.

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/Architecture/AgentBase.cs
public abstract class AgentBase(IChatClient chat, IAgentTelemetry telemetry)
{
    protected async Task<T> ExecuteAsync<T>(string agentName, Func<Task<T>> work, CancellationToken ct)
    {
        using var activity = telemetry.Start(agentName);
        try { return await work(); }
        catch (Exception ex) { telemetry.Fail(agentName, ex); throw; }
    }
}

What happened?

  • AgentBase wraps work with telemetry activities.
  • Concrete CRM agents inherit consistent logging and error handling.

Practice next

  1. Create AgentBase with telemetry hooks.
  2. Inherit CrmCopilotAgent from AgentBase.
  3. Define IAgentTelemetry with OpenTelemetry exporter.
  4. Add optional IInputGuard on AgentBase before work runs.
  5. Return AgentResult with status enum instead of raw payloads.

Remember

AgentBase standardizes telemetry and error paths. Agent architecture separates plan, tools, and format steps. Concrete agents stay thin inside ExecuteAsync.

Ops dashboard

SRE needs p95 latency per agent name across tenants.

Outcome: AgentBase telemetry feeds one Grafana board for all AgentNest agents.

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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