How OpenTelemetry and Jaeger expose silent failures in modern systems

Performance issues in distributed systems often surface through symptoms like slow APIs, stalled queues, or unexpected outages.

When they occur, teams typically turn to logs, dashboards, and metrics, but the underlying cause can remain hidden inside interconnected services, queues, and asynchronous flows.

This is where distributed tracing shifts from a technical tool to a strategic capability.

At Itsavirus, we build and modernise complex systems for clients across sectors. Across those projects, one principle stays consistent:

You can’t optimise what you can’t see. And you can’t scale what you can’t trace.

Distributed tracing gives you the visibility that modern architectures rely on.

This article explains how OpenTelemetry + Jaeger form the foundation of that visibility, and why leaders should treat tracing as an engineering requirement, not an optional add-on.

Why traditional monitoring falls short

Digital systems rarely remain static.

Monolith → services → microservices → event-driven → AI-driven → hybrid everything.

With each step forward, understanding what is happening becomes harder.

Traditional monitoring tools each offer part of the picture:

• Logs → individual events
• Metrics → aggregated behaviour
• APM tools → surface-level insights

But none of these answer the crucial question:

“Where, exactly, does a distributed request slow down or fail?”

If one user action touches:

• 9 services
• 3 databases
• 2 queues
• and triggers retries in the background

— where do you investigate?

This is the gap distributed tracing closes.

What OpenTelemetry brings to the table

OpenTelemetry (OTel) is often mistaken for another monitoring tool.

It isn’t.

It is a standard, a common language that unifies telemetry across your entire system.

OTel provides:

• consistent instrumentation
• support for major languages and frameworks
• unified collection of traces, metrics, and logs
• automatic instrumentation where available
• a vendor-neutral foundation that outlives specific platforms

In modern architectures — especially those involving AI agents, orchestrators, and asynchronous workflows — this standardisation eliminates blind spots.

It ensures every component speaks the same telemetry language.

Why Jaeger completes the system

OpenTelemetry gathers the signals.

Jaeger turns them into understanding.

Originally developed at Uber, Jaeger provides an end-to-end view of:

• request latency across services
• bottlenecks in distributed flows
• where failures originate (not just where they surface)
• queue delays, timeouts, and retries
• cross-service dependencies

This gives engineering, product, and operations teams a shared reality — a complete trace of how a request moves through your system.

For modern, interconnected platforms, this level of visibility is not a luxury.

It’s foundational.

A practical architecture we implement for clients: OTel + Jaeger

Application Code
│ (Auto + Manual Instrumentation)
▼
OpenTelemetry SDK
▼
OTel Collector (Agent + Processor)
 - Filtering
 - Enrichment
 - Sampling
▼
Exporter
▼
Jaeger Backend
 - Storage
 - Query Engine
 - UI
▼
End-to-end Traces for Analysis



Why this architecture works

• Instrument once, export anywhere → No vendor lock-in
• Highly scalable → Horizontal collectors, tuned sampling, batching
• Operational resilience → Telemetry continues even under load
• Future-proof → Works with Jaeger, Tempo, Elastic, AWS X-Ray, and more
• Consistent visibility → Every service follows the same standard

This becomes the backbone of reliability for many of our clients — especially during modernisation or large-scale AI initiatives.

What teams gain immediately

1. Faster, more accurate root-cause diagnostics

Teams no longer guess where issues originate.

They follow the trace.

2. Clarity about system behaviour and dependencies

Every service call.

Every hop.

Every latency spike.

3. Performance optimisation informed by evidence

Engineering effort shifts from intuition to data-led decision-making.

4. Lower risk during legacy modernisation

In Strangler Fig modernisation, tracing exposes parity issues early — before they reach production.

5. Better alignment between engineering, product, and operations

Traces become the shared source of truth.

Not opinions. Not assumptions.

When to implement distributed tracing

If any of the following are true, the right time is before the next incident:

• You operate microservices or event-driven systems
• You are modernising a legacy platform
• Multiple teams own different parts of the stack
• You are building AI workflows, orchestrators, or agent pipelines
• Latency appears intermittently and is hard to reproduce
• Logs and metrics aren’t giving clear answers
• You want reliability to scale with your customer base

Tracing isn’t something you introduce after things break.

It’s what prevents them from breaking in the first place.

The Itsavirus way

We don’t “add Jaeger” to your system.

We design a complete observability strategy that matches your:

• architecture
• deployment model
• DevOps maturity
• AI or data workloads
• security and compliance requirements
• long-term transformation roadmap

Our approach is built on:

Simplicity, strategic execution, and long-term reliability.

Conclusion

Modern systems fail in the gaps, between services, queues, and asynchronous processes.

Distributed tracing makes those gaps visible.

When implemented with OpenTelemetry + Jaeger, it becomes one of the most valuable capabilities for organisations aiming for reliability, scalability, and clarity.

If you’re exploring modernisation or building systems where reliability matters, we’re happy to share how tracing can become part of your architecture.

No pressure. No push.

Just clarity.

Let’s talk and contact our representatives here