Introduction
As applications become more distributed, cloud-native, and AI-driven, understanding what is happening inside a system becomes increasingly difficult. Modern applications often consist of APIs, microservices, databases, message queues, AI models, vector databases, and external services working together to deliver a single user experience.
When something goes wrong, developers need answers quickly.
Questions such as:
Why is the application slow?
Which service is causing failures?
Why did the AI agent generate an incorrect response?
Which workflow consumed the most tokens?
Why are costs suddenly increasing?
Which tenant is experiencing issues?
These questions cannot be answered effectively using traditional logging alone.
This is where observability becomes essential.
Observability enables teams to understand the internal state of a system by collecting and analyzing telemetry data. When combined with AI, observability platforms can automatically detect anomalies, identify root causes, predict failures, and provide actionable insights.
In this article, you'll learn how OpenTelemetry works, how to implement observability in .NET applications, and how AI can transform raw telemetry data into meaningful operational intelligence.
What Is Observability?
Observability is the ability to understand the internal behavior of a system using telemetry data.
Traditional monitoring often focuses on predefined metrics.
Example:
CPU Usage
Memory Usage
Disk Space
Observability provides deeper insights.
Example:
What happened?
Why did it happen?
What will happen next?
This enables more effective troubleshooting and optimization.
The Three Pillars of Observability
Modern observability is built on three primary components.
Logs
Logs record individual events.
Example:
User Login Successful
Logs provide detailed information about application behavior.
Metrics
Metrics provide numerical measurements.
Example:
Requests Per Minute:
500
Metrics help identify trends.
Traces
Traces show how requests move through distributed systems.
Example:
API
|
v
Service
|
v
Database
Tracing helps identify bottlenecks and failures.
Why AI Applications Need Observability
AI systems introduce additional complexity.
Example architecture:
User
|
v
API
|
v
AI Agent
|
v
Vector Database
|
v
LLM
Failures can occur at multiple points.
Without observability:
Problem Exists
Unknown Cause
With observability:
Problem Detected
Root Cause Identified
This dramatically improves operational efficiency.
Understanding OpenTelemetry
OpenTelemetry is an open-source observability framework.
It provides standardized APIs for collecting:
Logs
Metrics
Traces
Benefits include:
Vendor neutrality
Broad ecosystem support
Consistent telemetry collection
Cloud-native integration
OpenTelemetry has become the industry standard for observability.
OpenTelemetry Architecture
A typical architecture looks like this:
Application
|
v
OpenTelemetry
|
v
Collector
|
v
Observability Platform
Telemetry flows through the collector to analysis systems.
Installing OpenTelemetry
Create an ASP.NET Core project.
dotnet new webapi -n AiObservabilityDemo
Install OpenTelemetry packages.
dotnet add package OpenTelemetry.Extensions.Hosting
dotnet add package OpenTelemetry.Instrumentation.AspNetCore
dotnet add package OpenTelemetry.Exporter.OpenTelemetryProtocol
These packages enable telemetry collection.
Configuring OpenTelemetry
Configure OpenTelemetry in Program.cs.
builder.Services
.AddOpenTelemetry()
.WithTracing(builder =>
{
builder.AddAspNetCoreInstrumentation();
});
This captures request tracing automatically.
Understanding Distributed Tracing
Distributed tracing tracks requests across services.
Workflow:
Client
|
v
API
|
v
Service
|
v
Database
A trace links all operations together.
This helps identify performance bottlenecks.
Example Trace Flow
Consider an AI request.
User Question
Workflow:
Frontend
|
v
API
|
v
AI Service
|
v
OpenAI
The trace shows every step involved in processing the request.
Collecting Metrics
Metrics provide quantitative insights.
Examples:
Request count
Response latency
Error rate
Token usage
Active users
Example:
Requests:
10,000
Errors:
25
Metrics help measure system health.
Creating Custom Metrics
Custom metrics are often useful.
Example:
var meter =
new Meter("AiPlatform");
var tokenCounter =
meter.CreateCounter<int>(
"tokens_used");
Custom metrics can track AI-specific activity.
Monitoring AI Token Consumption
AI systems require additional telemetry.
Example:
Input Tokens:
1,000
Output Tokens:
500
Tracking token usage helps manage costs.
Common AI metrics include:
Tokens consumed
Model usage
Prompt size
Response size
These metrics support optimization efforts.
Logging AI Activity
Logs remain important.
Example:
_logger.LogInformation(
"AI request processed");
Useful log data includes:
User requests
Agent actions
Model selections
Tool invocations
Logs provide valuable diagnostic information.
Observing AI Agents
Agent workflows can be complex.
Example:
Goal
|
v
Reasoning
|
v
Tool Call
|
v
Response
Each step should generate telemetry.
This enables visibility into agent behavior.
Monitoring RAG Systems
Retrieval-Augmented Generation introduces additional components.
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