A retail e-commerce company faced challenges with their AI customer support agent as it struggled to handle real-world queries that differed from controlled testing scenarios. Users' informal and varied language led to inconsistent and often incorrect responses, increasing escalations to human agents. The solution involved implementing batch testing using actual customer queries from chat logs and support tickets. This approach allowed bulk testing and highlighted areas where the AI was failing, enabling the team to refine prompts and improve intent handling. The result was a more accurate, reliable, and user-friendly agent that better aligned with real customer behavior. The batch testing process in Microsoft Copilot Studio streamlined validation by allowing testing of multiple real-world scenarios simultaneously, thus reducing manual testing efforts and enabling rapid identification and resolution of response issues.
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