Tutorials ASP.NET Core with Agentic AI Tutorial

AI Analytics Platform — Complete Guide

AI Analytics 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 94 of 100

AI Analytics Platform

AI basics ✓Agents

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

What is this?

AI Analytics Platform is AgentNest module for natural language metrics, anomaly alerts, and scheduled executive briefs over warehouse data. It combines SQL, stats agents, and narrators.

Why should you care?

Product differentiation beyond copilots — customers buy insights layer on same AgentNest tenancy and audit.

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/Controllers/AnalyticsPlatformController.cs
[ApiController]
[Route("api/analytics/platform")]
public sealed class AnalyticsPlatformController(AnalyticsSupervisor supervisor) : ControllerBase
{
    [HttpGet("brief/weekly")]
    public Task<DashboardBrief> Weekly([FromQuery] Guid tenantId, [FromQuery] DateOnly week, CancellationToken ct) =>
        supervisor.ProduceAsync(tenantId, week, ct);

    [HttpPost("ask")]
    public Task<string> Ask([FromBody] AnalyticsQuestion q, MetricsNarratorAgent narrator, CancellationToken ct) =>
        narrator.AnswerAdHocAsync(q.TenantId, q.Question, ct);
}

What happened?

  • AnalyticsPlatformController serves scheduled weekly briefs and ad-hoc questions via MetricsNarratorAgent with warehouse SQL grounding.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Provision read-only Synapse or Postgres warehouse connection.
  2. Schedule Weekly brief job per tenant CEO timezone.
  3. Validate narrative numbers against SQL in post-processor.
  4. Add anomaly alert webhook when stats agent flags outlier.
  5. Embed brief charts as base64 in API response.

Remember

Analytics platform combines supervisor briefs and ad-hoc Q&A. Ground all numbers in parameterized warehouse SQL. Schedule weekly briefs per tenant timezone.

Executive Monday brief

CEO receives AgentNest weekly brief email with narrative and figures.

Outcome: Board prep time drops; figures match Power BI because SQL source is shared.

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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