In today's world of microservices, serverless functions, and complex AI pipelines, understanding application behavior has become a monumental task. When a single user request can trigger a cascade of events across dozens of services, pinpointing the source of a slowdown or an error feels like searching for a needle in a haystack.
Distributed tracing has emerged as the map for this complex new territory. But traditional tracing often comes with its own baggage: tedious manual instrumentation, code clutter, and the constant risk of incomplete data.
What if you could have complete clarity without the manual effort? It's time to move beyond old methods and embrace a new, automated approach. Introducing agentic workflows with trace.do—the key to unlocking effortless, code-driven observability.
Observability is essential, but the path to achieving it can be painful. Developers are often forced to:
This manual overhead, or "toil," slows down development cycles and turns a powerful diagnostic tool into a chore.
At trace.do, we believe observability shouldn't be an afterthought; it should be an integral, programmatic part of your application. This is the principle of Observability as Code. Instead of manually clicking through UIs or littering your code with boilerplate, you define your observability needs directly within your application using a clean, simple API.
Consider how simple it becomes to trace a complex business workflow, like processing an order:
import { trace } from '@do/trace';
async function processOrder(orderId: string) {
// Automatically trace the entire function execution
return trace.span('processOrder', async (span) => {
span.setAttribute('order.id', orderId);
// The trace context is automatically propagated
const payment = await completePayment(orderId);
span.addEvent('Payment processed', { paymentId: payment.id });
await dispatchShipment(orderId);
span.addEvent('Shipment dispatched');
return { success: true };
});
}
With a single trace.span wrapper, you've accomplished several things:
This is the power of agentic workflows.
The term "agentic" means our SDK acts as an intelligent agent on your behalf. You declare your intent—"I want to trace this block of code"—and the agent handles the complex implementation details for you.
This approach gives you the best of both worlds: the full power of distributed tracing with the simplicity of a single function call. You get complete clarity into your request flows, helping you diagnose performance bottlenecks and resolve issues faster across your entire stack.
New paradigms often bring new questions. Here are some common ones we hear:
Distributed tracing is a method used to profile and monitor applications, especially those built using a microservices architecture. It follows a single request as it travels through multiple services in your application. trace.do automates this process, providing a unified view of the entire journey. This complete picture helps you pinpoint latency issues, identify the root cause of errors, and deeply understand your system's behavior under real-world conditions.
Yes. Compatibility is a core principle for us. trace.do is built on OpenTelemetry (OTel) standards, the industry benchmark for observability. This means it works out-of-the-box with a wide range of popular frameworks, libraries, and observability platforms you already use, including Jaeger, Datadog, Prometheus, Honeycomb, and more.
trace.do provides SDKs for major programming languages that abstract away the complexity of manual instrumentation. Instead of wrestling with low-level OTel APIs, you use our simple trace.span wrapper. This agentic approach automatically captures traces, metrics, and logs, turning what was once a complex, error-prone task into a simple, maintainable, and programmatic workflow.
A 'span' is the fundamental building block of a trace. It represents a single, named, and timed unit of work within a larger operation, like an API call, a database query, or a function execution. Spans are connected in a parent-child hierarchy to form a complete trace, visually showing how different operations relate to each other over time and how they contribute to the total request duration.
Stop fighting with manual instrumentation and start shipping with confidence. By embracing agentic workflows and observability as code, you can spend less time instrumenting and more time innovating.
Ready to see how simple automated distributed tracing can be?
Visit trace.do to get started and bring complete clarity to your services.