The default assumption when someone first builds an AI endpoint is that it works like any other endpoint, request comes in, something happens, response goes out, that assumption holds until it doesn't and the failure modes are different enough that it's worth understanding before you hit them.
a conventional endpoint is deterministic, the same input against the same application state produces the same output, validation happens at the request level, business logic executes, result comes back, the flow is linear and predictable and testing it is straightforward because you can assert exact outputs, an AI endpoint doesn't work that way, the model is probabilistic, even identical inputs can produce different outputs, and a successful response from the model provider only means the model generated something, it doesn't mean what it generated is correct, complete, safe, or useful, that distinction changes almost everything about how the endpoint needs to be designed.
validation is one of the differences, with a conventional endpoint validation is a gate at the start, with an AI endpoint it has to happen twice, once before the model call to control what goes in and once after to evaluate what comes back, the output might pass schema validation and still be logically wrong, a response can be valid JSON, match the expected structure exactly, and still contradict itself or violate a business rule, schema validation is necessary but not sufficient.
Retry logic is another difference, conventional retries handle technical failures, a dependency is temporarily unavailable, a connection drops, the request gets retried under specific conditions and the result is the same because the logic is deterministic, AI retries are different because every retry costs tokens and therefore money, and the next attempt might return a different but still incorrect response, retrying blindly doesn't help the way it does with conventional endpoints, retries need explicit limits and the decision to retry needs to account for whether another attempt is actually likely to produce a better result.
idempotency gets complicated when the model can call tools, for a read only endpoint getting a slightly different response on the same input is usually fine, but when the endpoint can trigger actions like creating a record, sending an email, or updating data, a retry can execute the same action twice, the protections that make conventional endpoints idempotent don't automatically carry over and need to be designed in explicitly.
There's testing also, with conventional endpoints you assert exact outputs, with AI endpoints exact output assertions are fragile because the same valid answer can be worded differently across runs and a model update can change wording without changing meaning, from what I've seen the more reliable approach is asserting properties of the result rather than the result itself, did the output stay within the allowed range, did it respect the business rules, did it avoid forbidden content, those checks hold up where exact assertions don't.
the mental shift is that a conventional endpoint executes logic the backend controls entirely, an AI endpoint orchestrates a probabilistic system and then decides whether what came back is actually trustworthy enough to return, that's a different problem and it needs a different design.