Building AI into Integration Workflows

We introduced AI into our integration writing workflows to solve a recurring and deeply practical challenge: how to speed up development without compromising code quality, maintainability, or architectural clarity. The goal wasn’t a black-box shortcut, it was building automation engineers could trust, reason with, and improve on.

Here’s how that unfolded.

Start with reliability, not reinvention

It may sound simplest to ask the AI to write the code for an entire integration, but we found that when we have existing reliable code it is better to use that than to ask the AI to rewrite it.

We already have working clients for many APIs that handle the auth, pagination, rate limiting, and automatic retries.  These are designed to be used across different integrations.  When we need to create a new one, we just use a deterministic generator and deep API-specific experience.

So we decided to let the AI use our vetted clients, instead of making it write its own.

This foundation reduces the scope of the AI’s task. It only needs to generate the logic to fetch data from one API’s client, apply a transformation, and send it to another API’s client. 

We’ve seen that the AI’s code is more successful when it uses our clients.

Guardrails > Broad prompts

Early trials threw too much context at the AI: full client code with vague instructions. The results were inconsistent at best.

The shift came from introducing strict constraints:

• Only ask the LLM to provide code for one flow at a time, so an integration that needs to sync both orders and tracking would require two separate requests to the LLM.
• Only provide the details about the clients that are needed for the LLM interact with it, instead of the full code
  
  • Provide a method list that includes the types of the params and the returned objects.
  • Supplement that with a suggestion of the methods most likely to be relevant.
• Give the LLM very specific instructions on how to write the code instead of broad prompts.
  
  • Provide a code template for the flow.
  • Give step by step instructions on how to customize and fill out the template.

Every flow produced by the LLM is validated automatically by compiling it. If the compilation fails, the error is passed back to the model so it can correct the offending code. ​​It’s a controlled loop, not a firehose of speculative output.

We allow limited creativity only in isolated areas, like the transformation logic, config suggestions, and when supporting records need to be fetched. Even then, results are checked, and guardrails are in place.

‍Modularity makes this sustainable

The system is reliable because everything is modular and enforceable.

Here’s how the pipeline works:

• Pandium's pre-written client libraries give the LLM crucial information about the APIs' type structures. This allows for schema validation.  
• Each integration flow created by the LLM is focused and self contained. This allows the system to test that each flow can be compiled independently of any other flows.
• The scaffolding that runs all the flows is generated deterministically.

The AI doesn’t guess at structure. It writes logic within a clean system of contracts and expectations. That’s what keeps things readable, reliable, and testable.

Onboarding through real, working code

Customers can run the integration code on their Pandium sandbox as soon as it is generated, but they always have the opportunity to review and adjust it before merging.

In fact, having a junior engineer review the code made with this generator is a great way to shorten their learning curve in integration work.

Instead of starting with documentation and a blank file, they get a working, structured flow that reflects best practices in pagination, error handling, and transformation. It’s code they can inspect, adjust, and learn from.

It makes the development process more interactive because there are fewer static docs and more real patterns in action.

‍No black boxes

Generated flows live in version-controlled repos alongside everything else. They follow consistent patterns, are fully editable, and use understandable logic.

There’s no opaque syntax, no undocumented behavior, no mystery logic. If the AI output doesn’t work or isn’t ideal, an engineer can revise it like any other code.

This transparency is key to trust, and to long-term maintainability.

Final thoughts

The AI hasn’t replaced engineers, it’s just removed some of the repetition. It drafts logic we’d otherwise copy-paste for the fiftieth time.

But the value didn’t come from the model itself. It comes from everything around it: scoped tasks, reusable abstractions, typed methods, enforced structure, and a pipeline that keeps the quality bar high.

That’s what lets us move faster without giving up control.