Users may sometimes use abusive language or profanity towards the agent. The agent should not break character or respond in kind. Test cases with abusive language help ensure the agent follows policy (e.g., giving a standard polite response or warning, or escalating to a human). KPIs could be that the agent never uses profanity itself (unless perhaps it’s specifically designed for a very informal persona, which is unlikely in customer service) and that it responds to abuse in an acceptable way (maybe by saying “I’m here to help; I understand you’re upset” etc., as per design). Also, if abuse gets too much, does the agent know when to terminate the call or involve a supervisor? This can be part of the design and thus tested. The benchmark could be qualitative (review transcripts of abuse scenarios to ensure compliance) or even a simple flag that no matter what the user says, the agent’s emotion detection and response remains within acceptable bounds. From a performance view, also ensure that profanity in user input doesn’t degrade the ASR or NLP (e.g., some systems might censor or mask it and could lose meaning). The agent should still correctly interpret the user’s actual request even if it’s wrapped in angry words.