Tutorials ASP.NET Core with Agentic AI Tutorial

AI Multi-Agent Platform — Complete Guide

AI Multi-Agent Platform — 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 98 of 100

AI Multi-Agent Platform

AI basics ✓Agents

Agents · 2 — Build · ~10 min · Module 10: Enterprise AI Projects

What is this?

AI Multi-Agent Platform exposes supervisor orchestration, delegation, and message bus as managed AgentNest capability — tenants compose agent teams without writing C# supervisors.

Why should you care?

Advanced customers want researcher + writer + reviewer teams like internal AgentNest engineering patterns, configurable in UI.

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.MultiAgent/ConfiguredSupervisorHost.cs
public sealed class ConfiguredSupervisorHost(IAgentTeamLoader teams, IAgentDispatcher dispatcher)
{
    public async Task<TeamResult> RunTeamAsync(string teamId, AgentRequest request, CancellationToken ct)
    {
        var team = teams.Load(teamId);
        object? handoff = request;
        foreach (var member in team.Members)
        {
            handoff = await dispatcher.InvokeAsync(member.AgentName, member.AllowedTools, handoff!, ct);
        }
        return new TeamResult(teamId, handoff);
    }
}

What happened?

  • ConfiguredSupervisorHost loads team JSON definition and chains agents via dispatcher with per-member tool scopes — platformized multi-agent pattern.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Define agent team schema in SQL with ordered members.
  2. Build admin UI to assign CrmResearch + CrmWriter + CrmReviewer chain.
  3. Meter TeamResult runs separately from single-agent calls.
  4. Parallel member stage when team graph defines fork join.
  5. Team template marketplace for common CRM and ERP patterns.

Remember

Multi-agent platform runs configured agent chains with scoped tools. Load team definitions from SQL admin UI. Reuse dispatcher and delegation infrastructure.

Proposal quality team

Customer configures research, draft, compliance review agent chain for RFP responses.

Outcome: ConfiguredSupervisorHost runs nightly; compliance agent blocks unsourced pricing claims.

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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