AI replay testing reconstructs an earlier execution from recorded prompts, tools, inputs, events and state. Teams can rerun the same case with a different model, prompt version or runtime component and compare the resulting behavior.
A meaningful replay needs reproducible inputs and clear handling for nondeterministic models and external tools. Even when exact output cannot be reproduced, replay can show whether a change preserves task outcomes, validation rules and causal workflow structure.
ELI5
Replay testing means running a recorded AI task again so you can see how a change affects it. The same starting information and important events are reused, which makes the old and new results easier to compare.
For example, a team can replay a customer-support task after switching to a newer model. The wording may not be identical, but the team can still check whether the correct policy was followed and the same customer problem was solved.
What is needed to replay an AI agent run?
The system needs the original inputs, prompt and skill versions, model settings, tool interfaces, events and relevant stored state.
Will AI replay always produce identical output?
No. Models and external services can be nondeterministic, so replay often compares behavior and outcomes rather than exact text.
