Introduction

In modern systems especially those built with decoupled architectures data flows across multiple services without direct synchronous feedback. That design enables scale, but it also creates a silent risk: when data is incorrect, the error propagates unchecked.

At Meetlabs we believe quality cannot rely on late validation or implicit assumptions. System reliability begins where data is born. Designing with that premise changes the approach: it’s not about handling errors after the fact, but preventing them at the source.

The problem of decoupling

When errors have no feedback Decoupling solves scalability and availability, but it introduces a less obvious trade-off: loss of immediate feedback. When a producer sends data via queues or events, there is no synchronous validation that guarantees that data is correct from the consumer’s perspective.

This turns errors into something more dangerous: silent errors. They don't fail immediately, but they degrade the system progressively.

• Errors are not detected at the origin
• Feedback arrives late or never
• Traceability of the problem breaks down
• Debugging becomes reactive and expensive

No contract, no system: OpenAPI's structural role

Many teams talk about contracts but operate with implicit structures, the problem is that the implicit does not scale: every service interprets data its own way.

Defining contracts with OpenAPI is not mere documentation it's technical alignment. It's establishing a single source of truth for how data should look.

• Schemas define rules, not just formats
• Team interpretation drift is eliminated
• Structures can be reused across systems
• Real dependencies are made explicit

Automatic validation: when the schema becomes executable

The real value of a contract appears when it stops being static. Tools like ogen turn definitions into executable code, enabling requests to be validated automatically before they leave the system.

This is where design meets operation: what you defined in the schema becomes real enforcement.

• The schema stops being passive documentation
• Manual, scattered validations are reduced
• Consistency across requests is enforced
• Errors are prevented before they can propagate

Shifting responsibility: the producer as the control point

Delegating validation to the consumer is convenient but architecturally incorrect. It assumes errors are inevitable and that the system must adapt to them.

Moving validation to the producer changes that logic: the system is designed to avoid errors rather than to react to them.

• Cognitive load on consumers is reduced
• Unnecessary defensive logic is avoided
• Overall system quality improves
• Service evolution is simplified

Validation is not enough: design + validation as a system

Producer-side validation does not replace good data modeling. If the design is weak, validation only guarantees that the mistake is consistent—not that the system is correct.

Therefore, validation must be treated as an extension of design, not as a patch.

• Design defines what is correct
• Validation ensures the design is followed
• Both must evolve together
• Quality shifts from reactive fixes to structural integrity

Recommendations

• Define explicit contracts between systems from the start.
• Centralize schemas to avoid inconsistencies.
• Implement automatic validation based on those contracts.
• Avoid delegating validation to the consumer.
• Treat data quality as an architectural concern, not as debugging.

Conclusion

In decoupled systems, data quality cannot depend on late validations, designing validation at the producer transforms system reliability preventing errors before they exist and reducing complexity across the architecture.

Glossary

• Decoupled architecture: A design in which systems interact without direct dependencies on one another.
• Producer: A service that generates and sends data to other systems.
• Consumer: A service that receives and processes data.
• OpenAPI: A specification for defining API contracts in a structured way.
• Request validation: The process of verifying that data meets rules before it is sent.