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API Integration Automation With AI

TL;DR

• AI can read API docs, suggest mappings, and generate integration code. It will also miss schema mismatches, version quirks, and rate-limit gotchas.
• Use AI to draft and explore. You verify: Does this match the actual API? Have we handled errors and retries?
• Integrations are where systems meet. Bugs here ripple. Verify everything.

Integration engineering is connecting systems that weren't designed to connect. APIs, webhooks, message queues. AI can read OpenAPI specs, suggest mappings, and generate client code. It can also hallucinate endpoints, ignore version differences, and produce code that breaks in production. Your job: use AI for speed, own the verification.

What AI Can Do

Mapping and discovery:

• "Connect this CRM to this billing system." AI can read docs, suggest field mappings, and draft flows.
• Good first pass. You validate against real APIs. Docs are often wrong or outdated.

Client and connector code:

• "Generate a client for this REST API." AI can scaffold. You add auth, retries, error handling.
• Saves time. Never deploy without testing against the live API.

Schema translation:

• "Map this JSON to that format." AI can draft transforms. You handle edge cases: nulls, nested objects, type mismatches.
• Real data is messier than docs. Validate with sample payloads.

Documentation parsing:

• Large API docs are hard to navigate. AI can summarize, answer "how do I do X?" and suggest patterns.
• Verify. APIs change. AI trains on snapshots. Cross-check with current docs or the API itself.

What AI Misses

Schema reality:

• "Optional" fields that are required in practice. Enums that have undocumented values. Nested structures that explode. AI works from specs. Reality is messier.
• Test with real data. From production if possible.

Rate limits and quotas:

• AI might not suggest backoff, batching, or quota handling. You need to design for: "What happens when we hit the limit?"
• Integrations fail at scale. Plan for it.

Versioning and deprecation:

• APIs version. They deprecate. AI doesn't know your vendor's roadmap. You need to.
• Stay current. Monitor deprecation notices. AI won't.

Auth and security:

• OAuth flows, API keys, webhook signatures. AI can draft. It can also get them wrong. Security-sensitive code needs careful review.
• Never trust AI-generated auth code without audit.

The Workflow

1. Draft with AI — Mappings, client code, transforms. Get a starting point.
2. Verify against live API — Does it work? With real data? With errors? With rate limits?
3. Add resilience — Retries, backoff, circuit breakers, logging. AI often skips these. You add them.
4. Document — What did you assume? What breaks? Future you (and your team) need this.

Your Edge

• Cross-system awareness. You see how A affects B. AI optimizes locally. You optimize the connection.
• Vendor relationships. You know which APIs are reliable, which have quirks, which to avoid. That's tribal knowledge. Document it.
• Failure mode expertise. Integrations fail. You've seen the patterns. You design for them. AI suggests happy path. You add the rest.

Do This Next

1. Integrate one new API with AI — Draft the client and mapping. List every assumption you had to verify or fix. That's your checklist.
2. Test with real data — Use production-like payloads. Include errors, nulls, and edge cases. What did AI miss?
3. Document one integration — Assumptions, gotchas, failure modes. Share with the team. Build the knowledge base.