An AI workflow can deteriorate after a model update, a data change or a tool failure without going offline. A rollback plan defines how to limit exposure, continue essential operations and handle outputs already produced.
Define triggers beyond uptime
Use error rates, policy violations, quality evaluations, complaints and unusual behaviour changes. Availability does not mean acceptable operation.
Keep a known safe version
Version the model, prompt, retrieval configuration and tools as one release. Rollback should restore the entire compatible chain.
Prepare a reduced-function mode
Human review, read-only access or a simpler rule can keep the service useful. Clearly communicate its reduced capabilities.
Handle outputs already in progress
Pause queues, identify affected runs and review outputs produced during the problematic period. Reverting the system does not undo the past.
Rehearse and review the incident
Test timing, permissions and communication before an incident. After using the plan, improve the triggers and process rather than merely returning to the previous version.
This framework is an original DigitalNow editorial methodology.