What glue code actually gets you
A first AI workflow is usually a script: fetch the input, call a model, call an API to act on the result, log the outcome. For a low-volume, low-stakes process, this is a reasonable way to prove the workflow works before investing in more infrastructure.
The limitation shows up when a step fails partway through. A script that dies between the model call and the write-back leaves the business without a clear answer to a simple question: what state is this case actually in, and does it need to run again, resume, or be handled by a person?
The three symptoms that call for a real engine
Three conditions, alone or together, are a reliable signal that scripted glue code has reached its limit.
The first is partial failure with consequences. If a workflow calls three systems and the third one times out, does the business know whether the first two actions already happened? AWS's own Step Functions documentation frames this directly around retry and catch behavior: workflows should be able to retry a failed task or catch the failure and run an alternative step, rather than leaving the process in an unknown state.
The second is a step that runs for minutes, hours, or days. A workflow that waits on a document upload, a manager's approval, or a batch job finishing cannot live inside a single script execution. It needs the process state saved somewhere durable while it waits, then resumed later without re-running the steps that already completed.
The third is the need for an audit-grade execution history: which step ran, in what order, with what input and output, and why a branch was taken. That requirement usually appears once a workflow touches money, compliance, or customer-facing decisions, and it is expensive to bolt on after the fact if the original design never captured it.
What an orchestration platform is actually buying you
Purpose-built orchestration tools are not a heavier way to write the same script. They exist because tracking state, retries, and recovery across many concurrent, long-running processes is a genuinely hard problem, and mature platforms have already solved the parts that are easy to get wrong.
AWS Step Functions models a workflow as a state machine with typed states for tasks, choices, parallel branches, and waiting on a callback, and it keeps a visual, queryable execution history for every run. Temporal takes a different approach: it describes its model as letting a workflow be written as ordinary code while the platform captures state at every step, so a failed workflow resumes exactly where it left off instead of needing custom reconciliation logic.
Both approaches remove the same category of custom code a team would otherwise have to write and maintain by hand: a table tracking which step each case is on, a retry loop with backoff, and a recovery path for cases stuck mid-process. That is the actual cost being avoided, not just the appearance of using more serious tooling.
How to decide without over-building
Not every AI workflow needs this. A single-step classification task with no downstream write-back, or a low-volume internal tool where a failed run can simply be rerun by a person, does not justify the operational overhead of running and maintaining an orchestration platform.
The more useful exercise is to list the workflow's steps, mark which ones write to an external system or take longer than a few seconds, and ask what should happen if each one fails independently. If that list produces more than one or two cases where the answer is 'we would have to figure it out manually,' the workflow has already outgrown scripted glue code, whatever its current size.
This decision also sits upstream of governance. An orchestration layer with a real execution history makes the audit trail, retry policy, and recovery path visible and testable, which is exactly the foundation a later evaluation or compliance review will need. Building that structure early tends to cost less than retrofitting it after the workflow is already handling live cases, and the NIST AI Risk Management Framework's own emphasis on designing traceability into a system rather than adding it afterward applies just as much to the orchestration layer as it does to the model.