Why API Integration Is Harder Than It Looks
Connecting two systems sounds simple. In practice, it's where most technical debt accumulates. Here are the five mistakes we encounter in almost every rescue engagement.
1. No Error Handling Strategy
Most integrations are built for the happy path. When an API returns a 429 (rate limited) or 503 (service unavailable), the integration silently fails. Data goes missing, and no one notices for weeks.
Fix: Implement exponential backoff, dead-letter queues, and alerting from day one.
2. Ignoring Data Format Differences
System A stores dates as "DD/MM/YYYY." System B expects "YYYY-MM-DD." This isn't a bug. It is a data modeling problem, and it belongs in the integration layer rather than patched in the UI.
Fix: Build a canonical data model. Transform on ingest, not on display.
3. No Idempotency
If your integration runs twice, does it create duplicate records? If yes, you have an idempotency problem. This is especially dangerous in financial systems.
Fix: Use idempotency keys for every write operation. Design integrations to be safely re-runnable.
4. Hardcoded Credentials
API keys in source code. Passwords in environment files committed to Git. We've seen it all.
Fix: Use a secrets manager. Rotate credentials automatically. Never commit secrets.
5. No Monitoring
The integration works on day one. Six months later, the upstream API changes its response format. Your integration keeps running, only now it's writing garbage data.
Fix: Monitor response schemas, not just HTTP status codes. Alert on shape changes.
The AY Labs Standard
Every integration we build ships with retry logic, monitoring, alerting, and automated tests. Because an integration that works "most of the time" is worse than no integration at all.