Everything You Need to Know About OpenTelemetry Agents

If you’re reading this, chances are you’re already familiar with OpenTelemetry (OTel)—the open-source standard for collecting observability data. But what about OpenTelemetry agents? How do they work, and why do they matter?

This guide unpacks everything you need to know about OTel agents—where they fit in your stack, how to set them up, and common pitfalls to watch out for. Let’s get into it.

Understanding the Role of an OpenTelemetry Agent

An OpenTelemetry agent is a lightweight process that collects, processes, and exports telemetry data (traces, metrics, and logs) from your applications. Think of it as a middleman between your application and an observability backend like Last9, Prometheus, or Jaeger.

How OpenTelemetry Agents Fit into Your Architecture

An OpenTelemetry agent typically runs as a separate process or is embedded within the application process.

It automatically instruments your application where possible, gathers telemetry data, and forwards it to an OpenTelemetry Collector or a backend of your choice.

The main advantage of using an agent is that it abstracts away the complexity of manually instrumenting your code while ensuring consistency in the telemetry data collected.

Benefits of Using an OpenTelemetry Agent in Your Application

Before OpenTelemetry, teams had to integrate different libraries for logging, metrics, and tracing.

The result? A fragmented, inconsistent, and hard-to-maintain observability setup. OpenTelemetry fixes this by standardizing data collection, and agents make that process even smoother.

Some key benefits of using an OpenTelemetry agent:

• Minimal Code Changes: Agents can auto-instrument your application without modifying your code, saving development time.
• Standardized Observability: OpenTelemetry ensures vendor-neutral, consistent observability data that works across multiple platforms.
• Lower Performance Overhead: Since the agent efficiently handles telemetry data collection and processing, your application remains performant.
• Flexible Backend Choices: You can send collected telemetry data to Last9, Prometheus, Jaeger, Datadog, or any other supported backend, ensuring flexibility and avoiding vendor lock-in.

How OpenTelemetry Agents Work Internally

At a high level, an OpenTelemetry agent follows a structured workflow:

1. Instrumentation: The agent hooks into your application runtime to collect traces, metrics, and logs automatically. Depending on the programming language, this could involve bytecode manipulation (Java), middleware hooks (Node.js, Python), or explicit SDK initialization (Go).
2. Processing: The collected telemetry data transforms such as batching, filtering, and enrichment to improve efficiency and usability.
3. Exporting: Finally, the processed data is forwarded to an OpenTelemetry Collector or directly to an observability backend for storage and visualization.

Example: Instrumentation Flow in a Microservices Application

Let’s say you have a microservices-based application with multiple services communicating via HTTP and gRPC. An OpenTelemetry agent:

• Automatically instrument incoming and outgoing HTTP requests to capture traces without modifying service code.
• Collects system metrics like CPU and memory usage.
• Enriches trace data with contextual metadata, such as request IDs or user session data.
• Batches and exports traces efficiently to minimize network and CPU overhead.

Step-by-Step Guide to Setting Up an OpenTelemetry Agent

Here’s how to install and configure an OpenTelemetry agent for different programming languages.

Setting Up OpenTelemetry Agent for Java Applications

Run your application with the agent:

java -javaagent:opentelemetry-javaagent.jar \
     -Dotel.service.name=my-java-app \
     -Dotel.exporter.otlp.endpoint=http://localhost:4317 \
     -jar my-app.jar

Download the Java Agent:

curl -L -o opentelemetry-javaagent.jar https://github.com/open-telemetry/opentelemetry-java-instrumentation/releases/latest/download/opentelemetry-javaagent.jar

How to Configure OpenTelemetry for Python Applications

Run your app with instrumentation:

opentelemetry-instrument python my_app.py

Install OpenTelemetry dependencies:

pip install opentelemetry-distro opentelemetry-exporter-otlp

Setting Up OpenTelemetry for Node.js Applications

Configure the SDK:

const { NodeSDK } = require('@opentelemetry/sdk-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-http');

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: 'http://localhost:4317' }),
});
sdk.start();

Install OpenTelemetry packages:

npm install @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/exporter-trace-otlp-http

Running an OpenTelemetry Agent in Go

Initialize the agent in your Go app:

package main

import (
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace"
    "go.opentelemetry.io/otel/sdk/trace"
)

func main() {
    exporter, _ := otlptrace.New(nil)
    tp := trace.NewTracerProvider(trace.WithBatcher(exporter))
    otel.SetTracerProvider(tp)
}

Install dependencies:

go get go.opentelemetry.io/otel \
      go.opentelemetry.io/otel/exporters/otlp/otlptrace \
      go.opentelemetry.io/otel/sdk/trace

OpenTelemetry Agent vs. Collector: Key Differences and Use Cases

When setting up OpenTelemetry in your system, you’ll often encounter both OpenTelemetry agents and OpenTelemetry collectors. While they may seem similar, they serve distinct roles in your observability pipeline.

What is an OpenTelemetry Agent?

An OpenTelemetry agent is a lightweight instrumentation tool that runs alongside your application. It automatically collects telemetry data—traces, metrics, and logs—by hooking into your application runtime. The agent then processes and exports this data to a backend or an OpenTelemetry collector.

Key Characteristics of an OpenTelemetry Agent:

• Runs within the same environment as the application it monitors.
• Auto-instruments supported frameworks and libraries.
• Processes and exports telemetry data with minimal configuration.
• Best suited for applications that require minimal overhead and quick observability setup.

What is an OpenTelemetry Collector?

An OpenTelemetry collector is a separate, standalone service that receives telemetry data from agents, processes it, and then forwards it to an observability backend like Last9, Prometheus, or Jaeger. Unlike an agent, a collector is not tied to a single application instance and can aggregate data from multiple sources.

Key Characteristics of an OpenTelemetry Collector:

• Runs as an independent service, either as a sidecar, daemon, or centralized cluster component.
• Receives telemetry data from multiple agents or directly from applications.
• Performs data enrichment, filtering, and batching before exporting data.
• Best suited for centralized observability and reducing the overhead on application instances.

When to Use an OpenTelemetry Agent vs. a Collector

Example: A Distributed Microservices System

Let’s say you have a microservices-based architecture where each service generates traces, metrics, and logs. Here’s how you can integrate both agents and collectors effectively:

• Each microservice runs an OpenTelemetry agent to automatically instrument code and collect telemetry data.
• The agent exports data to an OpenTelemetry collector, which aggregates and processes telemetry from multiple microservices.
• The collector then forwards the processed telemetry data to a backend like Last9 for storage, analysis, and visualization.

This setup ensures a scalable, centralized observability pipeline while keeping performance overhead low on individual services.

6 Common Pitfalls When Using OpenTelemetry Agents (And How to Avoid Them)

OpenTelemetry agents are great for observability, but getting them right requires more than just dropping them into your stack. Many teams run into issues that impact data quality, performance, and security.

Let’s dig into the most common pitfalls and how to sidestep them effectively.

1. Misconfigured Exporters: Data Goes Nowhere

It’s easy to assume your telemetry data is flowing as expected—until you realize your backend is empty. The most common culprit? Misconfigured exporters.

What Goes Wrong:

• Incorrect endpoint URLs or ports (especially in distributed systems).
• Missing or incorrect authentication credentials (e.g., API keys, tokens).
• Exporter formats don’t match the backend’s expected structure (e.g., trying to send OTLP data to a backend that expects Prometheus format).

How to Avoid It:

• Verify connection settings by testing with a simple cURL request before deploying.
• Use structured logging on your agent to confirm successful exports.
• Run a local OpenTelemetry Collector to act as a proxy and normalize data before sending it to your backend.

2. High Resource Consumption: When "Lightweight" Becomes Heavy

OpenTelemetry agents are designed to be efficient, but improper configurations can turn them into performance hogs.

What Goes Wrong:

• Excessive instrumentation—tracing every function call or collecting unnecessary metrics.
• High sampling rates—trying to capture 100% of traces in a high-throughput system.
• Unoptimized batching—sending every data point individually instead of batching efficiently.

How to Avoid It:

• Adjust sampling rates dynamically (e.g., reduce in high-traffic scenarios, increase for debugging).
• Use batching and compression to reduce network overhead.
• Profile the agent's resource usage using tools like otel-cli or Prometheus to ensure it's not overwhelming your system.

3. Security Blind Spots: Leaking Sensitive Data

Telemetry data can contain personally identifiable information (PII) or secrets if you’re not careful.

What Goes Wrong:

• Unintentionally capturing PII or API keys in logs and traces.
• Sending unencrypted telemetry data over the network.
• Using weak or no authentication for the OpenTelemetry Collector.

How to Avoid It:

• Define explicit data redaction rules in your instrumentation.
• Enable TLS encryption for telemetry transport.
• Authenticate your collector using mTLS or API tokens to prevent unauthorized access.

4. Lack of Observability for the Agent Itself

Your OpenTelemetry agent is supposed to improve observability—but are you monitoring it?

What Goes Wrong:

• Silent failures in the agent cause missing or incomplete data.
• Unhandled errors in exporters lead to lost telemetry.
• No visibility into agent restarts or crashes.

How to Avoid It:

• Enable agent logs and metrics and send them to a separate observability system.
• Set up alerting on agent downtime or export failures.
• Use distributed tracing to track agent behavior and detect anomalies.

5. Over-reliance on Auto-Instrumentation

Auto-instrumentation is great, but it’s not magic. It doesn’t cover everything, and blindly relying on it can lead to missing critical insights.

What Goes Wrong:

• Important business logic isn’t traced because auto-instrumentation only captures framework-level calls.
• Auto-instrumented traces lack contextual metadata, making them harder to analyze.
• Inconsistent instrumentation when mixing auto and manual methods.

How to Avoid It:

• Manually instrument key business logic where auto-instrumentation falls short.
• Use span attributes and baggage to enrich traces with business-relevant metadata.
• Standardize instrumentation across services to ensure consistency.

6. Deploying Without a Staging Test

Shipping an OpenTelemetry agent straight to production without validation can lead to surprises—like missing telemetry, high CPU usage, or incorrect data formats.

What Goes Wrong:

• Incompatibility with the application due to untested instrumentation.
• Unexpected performance impact in production.
• Silent failures lead to incomplete observability.

How to Avoid It:

• Deploy to a staging environment first and compare telemetry with expected outputs.
• Use chaos engineering to simulate failures and test observability coverage.
• Benchmark resource usage before and after deployment.

Best Practices for Using OpenTelemetry Agent

1. Use the OpenTelemetry Collector: Instead of directly exporting telemetry data from your app, use an OpenTelemetry Collector to buffer, transform, and export data efficiently.
2. Enable Auto-Instrumentation: Take advantage of built-in instrumentation where available to reduce manual effort.
3. Optimize Sampling: Avoid collecting excessive trace data by configuring sampling strategies appropriately.
4. Monitor Agent Performance: Ensure the agent doesn’t introduce significant overhead by tracking CPU and memory usage.
5. Secure Your Telemetry Data: Use encryption and avoid exposing sensitive information in traces to maintain security compliance.

Conclusion

OpenTelemetry agents play a crucial role in collecting and exporting observability data, acting as the bridge between your application and your backend of choice. Setting them up correctly ensures you get reliable, high-quality telemetry without unnecessary overhead.

Observability isn’t just about collecting data; it’s about making that data work for you. And with OpenTelemetry agents in place, you’re well on your way.

FAQs

Can OpenTelemetry agents work with any observability backend?

Yes, OpenTelemetry agents support multiple backends, including Last9, Prometheus, Jaeger, and Datadog. You can configure the appropriate exporter to match your backend.

How much overhead do OpenTelemetry agents add to my application?

There’s a small overhead, but OpenTelemetry agents are optimized for minimal impact. Proper sampling and batching strategies can further reduce performance costs.

Do I need to modify my application code to use OpenTelemetry agents?

In many cases, no. OpenTelemetry agents support auto-instrumentation for several languages, reducing the need for code modifications.

How do I troubleshoot issues with OpenTelemetry agents?

Enable debug logs, verify network connectivity to the backend, and check configuration settings for errors.

Is OpenTelemetry ready for production use?

Yes, OpenTelemetry is widely adopted and production-ready, with strong community and enterprise support.