Integrations usually begin with a straightforward goal.
Connect two systems. Move data between them. Keep the systems synchronized.
During development everything appears to work well. Requests succeed, responses contain the expected data, and workflows behave correctly.
Then the integration runs in production.
That is when the real problems begin.
External systems are unpredictable
Third-party platforms change frequently.
APIs evolve. Fields appear or disappear. Rate limits change. Authentication mechanisms are updated.
Even small changes can break assumptions inside an integration.
A field that was always present suddenly becomes optional. A response that used to contain ten results now contains thousands.
Integrations that assume stability eventually fail when those assumptions stop holding.
Data is rarely clean
Production data is often inconsistent.
Different systems represent the same concept in slightly different ways. Formats vary. Fields contain unexpected values.
An integration must reconcile those differences.
Without careful validation and normalization, small inconsistencies can cascade through multiple systems.
Partial failures are common
Many integrations involve multiple steps.
An order might be created in one system, synchronized to another, and then trigger additional workflows.
If one step fails while others succeed, systems can become out of sync.
Designing integrations to detect and recover from these states is critical.
Observability is essential
Integration failures are often silent.
A webhook might stop firing. A background job might retry indefinitely. Data synchronization might gradually fall behind.
Without monitoring and reconciliation processes, these problems can persist unnoticed.
Integrations rarely fail because of a single mistake.
They fail because real production environments are more complex than development environments.
Designing integrations with that complexity in mind is the key to making them reliable.