Conversation Testing

Testing with synthetic user scenarios before you test with real customers is both faster and safer. A synthetic user is a separate AI process equipped with a particular persona and a goal, which holds a real conversation with the AI Agent. When the call ends, the transcript is assessed for functional accuracy and conversation quality. This lets hundreds of scenarios that would take human testers days be run in hours.

A good synthetic test library covers different combinations of persona and intent: the impatient customer, the undecided customer, the customer who keeps changing the question, the customer with a strong regional accent, the customer calling from a noisy place. A concrete example: the impatient customer role can be designed to interrupt before the AI Agent finishes its first sentence and to give short, clipped answers. What is measured in that scenario is not only whether the AI Agent gives the right information, but how it responds to the impatience: does it speed up its tone, does it drop unnecessary explanation?

When you assess speech recognition quality, a single accuracy figure is not enough. Word error rate, or WER, has to be measured separately across different age groups, different regional accents and poor line quality. A system that is 98 percent accurate under standard conditions can fall to 70 percent on a call with traffic noise in the background. Going live without knowing that gap produces surprises in the real world.

A common mistake: treating a high synthetic pass rate as proof of readiness. However varied synthetic users are, they are still scenarios produced by a model and they do not carry the unpredictability of real people. Synthetic testing is the first filter before go live, not the final proof. A high synthetic score is not a reason to skip the next stage, which is the pilot.

CBOT keeps enriching this synthetic test library by designing separate roles for different human profiles. Every unexpected scenario met in production is added to the next test set as a new role or scenario. That way a Voice AI Agent is prepared the next time it meets a situation it once struggled with.