Exactly. Once you start having to sanitize inputs, you're in data processor territory, and all the compliance baggage that comes with it. It's the difference between using a local linter plugin and making a POST request to an external API.
That structural generation point is where I've drawn my own line. If the AI is assembling the bones of a request/response mapping or a webhook payload spec, I now have to audit not just for correctness but for architectural fit. Last month I caught one drafting a webhook retry logic that would have hammered a client's endpoint with exponential backoff starting at 5 seconds. Technically correct, but totally inappropriate for their rate-limited API. The AI didn't know their SLA constraints.
So my disclosure statement now explicitly calls out "structural drafting" as a risk category. It tells the client there's a layer of intent they need to review, not just grammar.
APIs are not magic.
That "structural drafting" category is a perfect label for the critical transition. It's the moment the LLM moves beyond syntax and starts making *architectural decisions*, like your exponential backoff example.
This forces a validation burden we often overlook: we're not just checking for correctness against a static spec, but for *fitness* within a dynamic system with constraints the model can't see. It's akin to the difference between verifying a SQL query's syntax and verifying it won't cause a deadlock in your specific concurrency model.
Your disclosure approach maps to this well. I'd add that the client needs to understand the validation method isn't a standard code review; it's a full context reinjection. You're not saying "I checked the logic," you're saying "I had to re-apply all our internal SLAs, rate limits, and cost constraints to this generated structure." That's a materially different, and more expensive, quality gate.
Measure twice, cut once.
That's a really good point about the validation cost. I work with Salesforce data a lot, and the idea of >factual drift in logic< is scary with something like a flow or report formula.
Even a subtle change in how a data field is grouped or filtered could change an entire commission calculation. Spot-checking seems fine until you realize the error is in the underlying rule you didn't think to test.
So your disclosure becomes a promise about your test suite, not just your honesty. Do you think there's a threshold where the validation work is so high that it's just better to write the logic yourself from scratch?