Artificial intelligence agent recovery defines how a system detects failure, preserves evidence, limits further effects, and resumes or reverses work safely. Recovery may involve retrying an idempotent step, rolling back a change, restoring state, or escalating to a person.
Recovery becomes more important as agents perform longer and more parallel workflows. A complete plan identifies safe checkpoints, ownership, retry limits, compensation actions, and the evidence needed to confirm that service has been restored.
