This is related to the first point in this section but worth emphasizing as a separate benchmark if the user base is global or diverse. Test the agent on non-standard accents and dialects (including code-switching where a user might mix languages). The AI’s training data often determines how well it handles these. You might use specific datasets (e.g., Indian English, African American Vernacular English, etc.) and measure comprehension accuracy. We know that without exposure, ASR systems see higher WER on unfamiliar accents​

, so this test essentially quantifies that for your system and tracks improvements as you add more diverse training data or models like OpenAI Whisper (which is trained on many languages) in the pipeline. A target could be set, such as reducing the WER gap between standard accent and heavy accent by Z%. If the agent serves multiple languages, evaluate its language detection and switching capabilities as well (does it seamlessly handle a bilingual user?). This ensures edge cases of multilingual callers are handled.