How to test AI-generated API integrations against real APIs

AI coding tools can rapidly generate API integrations, but reliable testing remains difficult because customer accounts, permissions, data, rate limits, and webhook behavior vary, while generated mocks may fail to expose implementation errors. Nango’s integration-building workflow combines API research, implementation patterns, live dry runs, schema validation, deployment tools, and regression testing to help agents build and verify integrations against real provider accounts. Using a HubSpot contacts sync with 250 seeded records, the process begins by defining independent expected results, then running the integration live to identify issues such as missing pagination, which initially returned only 100 contacts before being corrected to retrieve all records across three requests. The workflow recommends testing variations including optional fields, empty accounts, alternative customer accounts, field mappings, retries, and rate-limit failures, then recording verified API responses for repeatable regression tests while keeping fixtures unchanged during implementation. After deployment, logs and customer-facing application data should be checked to confirm the integration behaves as expected in production, with similar practices applying to API actions used by AI agents.

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