Tutorials ASP.NET Core with Agentic AI Tutorial

AI Enterprise Architecture — Complete Guide

AI Enterprise 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 89 of 100

AI Enterprise Architecture

AI basics ✓Agents

Agents · 2 — Build · ~10 min · Module 9: AI System Design and Architecture

What is this?

AI enterprise architecture is the full blueprint — business capabilities, data flows, security zones, and technology choices for AgentNest at org scale. It aligns CRM, ERP, hospital, and platform teams.

Why should you care?

Without EA alignment, hospital team picks Qdrant while CRM picks Pinecone — ops inherits two runbooks and split billing.

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).

// docs/enterprise-architecture/agentnest-capability-map.json
{
  "capabilities": {
    "crm.copilot": { "agents": ["CrmCopilotAgent"], "stores": ["postgres", "pgvector"], "models": ["gpt-4o-mini"] },
    "hospital.assistant": { "agents": ["HospitalAssistant"], "stores": ["qdrant"], "models": ["gpt-4o"] },
    "erp.explain": { "agents": ["VarianceReasoner"], "stores": ["postgres"], "models": ["gpt-4o-mini"] }
  },
  "sharedPlatform": ["audit", "quota", "apim", "keyvault"]
}

What happened?

  • Capability map JSON documents which agents, stores, and models each business domain uses plus shared platform services.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Maintain capability map reviewed quarterly by architects.
  2. Standardize IVectorStore adapters — avoid N vector engines without cause.
  3. Map data classification to network zones in diagram.
  4. Add dependency graph between capabilities and external systems.
  5. Link each capability to SLO and cost center code.

Remember

Enterprise architecture maps capabilities to shared AgentNest platform. Standardize cross-cutting audit, quota, identity. Keep capability map current for ops and sales.

Architecture board approval

New analytics capability proposed with separate OpenAI subscription.

Outcome: EA review routes through shared platform — one Key Vault, unified audit, approved design.

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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