Tutorials ASP.NET Core with Agentic AI Tutorial

AI Observability — Complete Guide

AI Observability — 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 68 of 100

AI Observability

AI basics ✓Agents

Agents · 2 — Build · ~10 min · Module 7: Cloud-Native AI and DevOps

What is this?

AI observability unifies metrics, logs, traces, and eval scores for agent systems. AgentNest exports OpenTelemetry to Azure Monitor with dashboards for tokens, tool errors, and RAG hit rate.

Why should you care?

Without observability, CRM copilot degradation shows up only as angry tickets — not as actionable spans.

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).

// Program.cs OpenTelemetry
builder.Services.AddOpenTelemetry()
    .WithTracing(t => t.AddSource("AgentNest.Agents").AddAspNetCoreInstrumentation())
    .WithMetrics(m => m.AddMeter("AgentNest.Ai").AddRuntimeInstrumentation())
    .UseAzureMonitor();

What happened?

  • OpenTelemetry registers AgentNest agent ActivitySource and custom meter, exporting traces and metrics to Azure Monitor.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Instrument agents with ActivitySource and Meter("AgentNest.Ai").
  2. Create workbook: p95 latency, tokens/min, tool error rate.
  3. Correlate App Insights traces with AiAudit rows via operation_Id.
  4. Add custom metric rag.hit_rate from RagPipeline.
  5. Export same telemetry to Grafana for hybrid cloud tenants.

Remember

OpenTelemetry traces and metrics feed Azure Monitor. Dashboard tokens, tools, RAG, and HTTP together. Link traces to audit records for incident response.

Incident postmortem

ERP agent tool errors spike after plugin deploy.

Outcome: Trace view shows gl.fetch_balance failures; rollback pinpoints bad plugin version.

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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