And why it has to be treated as a development requirement
In many systems, observability fails to evolve with your system, even though it is not completely missing. It is simply incomplete.
This is rarely visible during development, and often not immediately apparent in production.
In many development environments, observability is still not treated as a real development requirement. It is something that teams intend to add later, or something that is implemented only where it seems necessary at the moment.
From a delivery perspective, this often works. Features are implemented, services are connected, and the system behaves as expected. At that stage, nothing appears to be missing.
The problem is that systems do not remain in their original state. They evolve continuously, and when observability does not evolve with them, gaps start to appear. These gaps are not immediately visible, but they become critical under the right conditions.
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Where things start to go wrong
During development, decisions are made based on the current context of the system. A service is considered non‑critical, so it is not instrumented. A legacy component already exists, so it is reused without modification. An integration is straightforward, so observability is not prioritised.
These decisions are not mistakes. They are reasonable at the time.
The issue is that they are made with a static view of the system, while the system itself is dynamic.
Over time:
- new features start relying on existing services
- previously low‑traffic components receive more load
- integrations activate functionality that was rarely used before
- business logic accumulates in places that were not designed for it
What was once a minor component becomes part of a critical execution path. Observability, however, remains exactly where it was originally implemented.
Case: when a previously minor service becomes critical
A common scenario illustrates this clearly.
An application consists of multiple backend services. Core components are already instrumented, so metrics, logs, and traces are available where teams expect issues to occur.
At the same time, one specific service is left without observability. Initially, this does not cause any problems because the service:
- handles a limited responsibility
- has low usage
- is not performance‑sensitive
Because of this, adding observability is not prioritised.
As the system evolves, this changes.
A new feature is introduced that depends on this service. Request volume increases, and the service becomes part of a larger execution flow. Gradually, it shifts from being optional to being critical.
However, observability is still missing.
When failure actually appears
The system continues to operate normally until something changes. This can be a new deployment, a change in behaviour, or simply a higher‑than‑usual load.
At that point, the effects start to show.
Response times increase across the system. Certain operations become noticeably slower, and timeouts appear in places that previously worked without issues. From a system perspective, the degradation seems general rather than localised.
Teams begin analysing the situation based on what they can observe. They look at API latency, frontend behaviour, and the performance of instrumented backend services. The dashboards show real issues, but these issues are only the visible consequences of a hidden problem.
The actual source of the issue is inside the service that was never instrumented.
Because that component has no metrics, does not appear in traces, and does not emit meaningful logs, it behaves as a blind spot. It exists in the system, but not in the data used to understand the system.
Why this slows everything down
Once the investigation starts without visibility into the real source, the process becomes indirect. Teams do not analyse the failing component, because it does not appear in any observable flow.
Instead, they focus on the parts of the system that are measurable. They scale services that are under load, optimise components that appear slow, and try to stabilise the system from the outside.
These actions are based on incomplete information.
The result is not just a slower resolution, but a fundamentally less effective one. The system improves temporarily or stabilises partially, but the underlying issue remains in place. When similar conditions occur again, the same behaviour returns.
This is where the real cost of missing observability appears. It is not only in the time it takes to fix an issue, but in the quality of the decisions made while trying to fix it.
The broader impact
The impact of missing observability is not limited to slower technical analysis.
When the source of a problem cannot be clearly identified:
- incident resolution takes longer, increasing the duration of service degradation
- decisions are made based on incomplete information, often leading to unnecessary changes or scaling
- issues are more likely to repeat, because the underlying cause is not fully understood
- overall confidence in the system decreases, both from a technical and a business perspective
These effects are cumulative. Over time, they increase both operational cost and risk, even if the system appears stable under normal conditions.
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Where this most often happens
The highest risk does not come from new components. It usually comes from parts of the system that were never revisited.
Legacy or “forgotten” services are especially problematic because they were not designed for their current role. They were created under different assumptions, often without expectations of scale, complexity, or criticality.
Over time, these services tend to accumulate additional responsibilities. New features are built on top of them, integrations reuse them, and dependencies increase. As their role expands, their impact grows, but their observability remains unchanged.
Another frequent case is when a new integration is built on top of an existing service. The service is reused as it is, without instrumentation being added. Initially, everything works, because usage is limited. Later, as the integration becomes more important, load increases and the same issue emerges. When something breaks, it breaks exactly where visibility is missing.
The underlying issue in development
The root cause is not technical complexity, but prioritisation.
Observability is often not treated as a formal development requirement, and is not given the same importance as functionality or performance.
It is seen as something that supports the system, rather than something that defines whether the system can be understood in production.
When observability is not explicitly required during development, it is implemented inconsistently. Some services include it because it is convenient or obvious, while others do not because there is no clear requirement to do so.
This leads to uneven coverage across the system, and over time, these uneven areas turn into blind spots.
What treating observability as a requirement actually means
Treating observability as a development requirement does not mean adding dashboards later or improving visibility over time. It means defining, during development, how a component will be observed once it is in production.
In practice, this implies that every service should include:
- structured and consistent logging that allows meaningful analysis
- basic metrics that reflect latency, error rates, and throughput
- traceability within distributed flows, so requests can be followed across services
- clear failure signals that can be used to detect issues in a reliable way
These elements are not enhancements. They are part of what makes a service complete.
If a component cannot be observed, it is only partially implemented from an operational perspective.
What needs to change
The key shift is simple in principle, but difficult in practice.
Observability must be treated as a requirement during development, not as an improvement to be added later. When a service is designed or modified, its observability should be considered at the same level as its functionality and performance characteristics.
This means that alongside defining what a service does, there must also be a clear understanding of how it will be observed in production. If a component becomes part of a critical path, its behaviour must be measurable, traceable, and diagnosable.
It also means that observability needs to evolve with the system. As dependencies change and usage patterns shift, previously non‑critical components need to be reassessed. If their role becomes more important, their observability must follow.
This is not a one‑time effort, but a continuous process aligned with how the system itself evolves.
What this means in practice
For development teams, this requires treating observability as part of the standard definition of a service.
This typically means that:
- observability is included in acceptance criteria
- service design includes considerations on how behaviour will be measured and analysed
- existing components are reassessed when their role in the system changes
- observability is extended when services become part of critical flows
It also requires revisiting parts of the system that were implemented under different assumptions. Components that were once considered non‑critical, but have since become essential, need to be evaluated from an observability perspective as well.
Why this becomes a production risk
Missing observability does not create immediate failures. It creates conditions where failures become difficult to understand.
The problem appears only when something changes: when load increases, when behaviour shifts, or when a defect is introduced. At that point, the part of the system without observability becomes the limiting factor in how quickly and accurately the issue can be resolved.
The system might appear to be observable, but in practice, it is only partially understood.
And in those situations, recovery speed and decision quality are determined by the least observable component, not the most advanced tooling in place.
Final perspective
Observability does not just improve systems. It determines whether they can be operated effectively when something goes wrong.
If it is not treated as a requirement, it will be missing exactly where it matters most.
And when that happens, the system does not fail everywhere.
Observability does not fail by producing visible errors.
It fails by removing the ability to understand what is happening inside the system.
It fails in the one place you cannot see.