Natural language is full of idioms and sarcasm that can trip up literal-minded AI. Test the agent with slang terms or colloquial phrases to see if it understands the intent. For example, a user might say "I need to ditch my old plan, it's crappy" meaning they want to cancel a service – does the agent glean that, or does it get confused by the slang “ditch” or the sentiment? Jargon and domain-specific terms are also important (especially in B2B contexts – e.g., in tech support, users might use technical lingo). The agent should ideally be trained on common slang/jargon, but testing will reveal gaps. Sarcasm detection is very hard (even for humans), but you can include a few sarcastic remarks in testing to check if the agent at least doesn’t take a clearly sarcastic statement at face value. For instance, user says "Oh great, you've been so helpful" in a frustrated tone – a naive agent might thank the user, but an advanced one might detect the dissatisfaction. While fully solving sarcasm might be out of scope, the agent should at least have strategies (like politely apologizing or clarifying). These scenarios can be manually evaluated or possibly flagged by sentiment tools (sarcasm often shows mismatch between positive words and negative tone).