Real-world artificial intelligence evaluation uses production or carefully governed field settings to test whether laboratory performance transfers to deployment. It can reveal context sensitivity, rare interactions, long-term feedback, coordination effects, and operational failures that are difficult to reproduce in a sandbox.
Field evaluation must not turn users or systems into unmanaged experiments. It requires informed governance, bounded exposure, privacy protection, rollback or recovery paths, continuous monitoring, incident response, and a design that does not interpret missing telemetry as evidence of safety.
