Tutorials ASP.NET Core with Agentic AI Tutorial

AI Workflow Automation — Complete Guide

AI Workflow Automation — 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 39 of 100

AI Workflow Automation

AI basicsAgents

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

What is this?

Workflow automation combines triggers, AI steps, and integrations — new CRM lead triggers enrich, score, and assign. AgentNest hooks queues and hosted services to agent endpoints.

Why should you care?

Hospital intake and ERP approvals repeat the same gates; automation removes manual copy-paste between systems.

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.Api/Triggers/LeadAutomationWorker.cs
public sealed class LeadAutomationWorker(IServiceScopeFactory scopes) : BackgroundService
{
    protected override async Task ExecuteAsync(CancellationToken stoppingToken)
    {
        await foreach (var leadId in LeadQueue.Reader.ReadAllAsync(stoppingToken))
        {
            using var scope = scopes.CreateScope();
            var agent = scope.ServiceProvider.GetRequiredService<ICrmCopilotAgent>();
            await agent.EnrichAndScoreAsync(leadId, stoppingToken);
        }
    }
}

What happened?

  • LeadAutomationWorker drains a channel queue and calls EnrichAndScoreAsync per lead.
  • Production swaps LeadQueue for Azure Service Bus.

Practice next

  1. Publish lead IDs to Channel or Service Bus on create.
  2. Register LeadAutomationWorker as hosted service.
  3. Make EnrichAndScoreAsync idempotent with lead version checks.
  4. Replace Channel with Azure Service Bus subscription.
  5. Add schedule trigger for nightly ERP reconciliation agent.

Remember

Automate AgentNest AI steps with queues and workers. Keep enqueue async; process in background services. Design idempotent agent actions for retries.

Inbound lead pipeline

Marketing floods CRM with webinar leads after an event.

Outcome: LeadAutomationWorker enriches and scores overnight without blocking UI.

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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