The ontology-based contextual AI evaluation (OB-CAIE) methodology addresses the lack of scientific rigor in AI testing by clearly defining what will be tested, using two ontologies to represent the problem space. This methodology allows for human judgment and tracing of failure points, making it a relevant development for AI agent builders.