Migrating an AI workflow without losing its acceptance criteria
A migration is successful when the replacement fulfils the application's task contract, not merely when a different model produces an output. Preserve the accepted behaviour, data boundaries and failure handling before changing a hosted or local AI component.
Inventory what the current system does
List input types, output formats, size limits, validation steps and downstream consumers. Include error behaviour, timeouts and the human review process. A prototype often depends on undocumented defaults that only become visible when a replacement behaves differently.
Capture representative permitted inputs and their accepted outcomes. Keep difficult cases and failures, not only favourable examples. Record the current model or endpoint version where available so a later comparison is not against a moving, unidentified baseline.
Define the replacement contract
Separate essential behaviour from implementation convenience. The replacement may use a different prompt structure or graph internally while still producing the required output format. Conversely, matching a file extension is not enough if the content is inaccurate or unsuitable for downstream use.
Write explicit checks for missing fields, invalid files and unsupported input. Do not silently coerce a failed result into something that appears valid. Keep a route for human review when the system cannot satisfy the acceptance condition reliably.
Review data and operating requirements
Map where each implementation stores inputs, outputs and logs. Use the relevant service's data-controls documentation for hosted endpoints and inspect local extensions for external calls. A change of model does not automatically remove contractual or data-handling obligations.
Review the applicable agreement and model licence with the responsible person. This page is not legal advice and does not assert that every migration is permitted under every contract. Record unresolved questions before sending real production data through the replacement.
Roll out with a reversible boundary
Compare the old and new routes on the same evaluation set in a safe environment. Measure acceptance, failure rate, completion time and operating effort separately. A cheaper or faster route may require additional review or may not support an essential feature.
Keep the previous implementation available until the replacement has met the agreed conditions. Record the switch date and rollback trigger. This revision removes the assumption that a business headline alone mandates migration; the decision is instead tied to the workload, evidence and an explicit recovery path.