Artificial intelligence replay testing turns recorded inputs, state and outcomes into repeatable test cases. Teams run those cases against a model, agent or procedure after a change to see whether known failures recur and whether successful behavior remains stable.
A safe replay environment disables production permissions and substitutes controlled dependencies where possible. Replay evidence is especially valuable for nondeterministic agents because a single pass is not proof of reliability. Multiple runs and explicit acceptance criteria improve confidence.
A replay should reproduce decision behavior without repeating real external actions. Removing production permissions prevents duplicate transactions or unsafe side effects.
Can replay testing prove an AI agent is safe?
No. It verifies behavior on recorded cases but cannot cover every future situation. It should be combined with permission limits, live monitoring and broader evaluation.
