No AI is 100% perfect, so when misunderstandings happen, the key is how the agent recovers. We should benchmark the agent’s ability to detect and correct misunderstandings. For example, if the agent hears something incorrectly (STT error) and gives an odd answer, does it recognize confusion (perhaps by the user’s response or by low confidence) and correct course? We can simulate misunderstandings: provide an input where we know the ASR or NLU is likely to err and see if the agent either asks for clarification or uses an alternate strategy (maybe the system prompt instructs the agent to double-check when not confident). A good metric is the clarification rate: how often does the agent ask clarifying questions or confirm what it heard. If it never does, it might be overconfident. If it does it in appropriate situations, that’s good. Another metric: recovery success rate – in cases where there was an initial misunderstanding, does the conversation eventually get back on track? (For instance, user: “I need support with my printer.” agent mishears “router”, starts router troubleshooting; user says “No, I said printer”; agent apologizes and switches to printer support – that would count as a recovered error). Logging these flows and analyzing them is important. We want a high recovery rate and low incidence of complete failure due to an error.