One score conceals the real risk
An average benchmark score cannot tell a business whether an AI workflow is safe to operate. Two systems with the same accuracy can have radically different consequences if one fails on harmless classification and the other fails on payment authority, privacy or regulated communications.
Specify failures before success
A useful evaluation begins with failure classes: unsupported claims, missed exceptions, incorrect routing, privacy leakage, prohibited execution and unrecoverable state changes. Each class needs an owner, a severity, an acceptable rate and a defined response when the boundary is exceeded.
Test the workflow, not only the model
Production outcomes depend on retrieval, permissions, policy logic, user interface, human review and downstream integrations. Model tests are necessary but incomplete. Teams should test stale evidence, conflicting documents, prompt injection, unavailable services, duplicate requests and attempts to bypass authority.
Publish what can be defended
Evaluation reports should state the dataset boundary, date, system version, acceptance threshold and unresolved limitations. A claim without those details is promotional evidence, not operational assurance. Morifar will label independent testing only when an external assessor has actually performed it.