In multi-turn interactions with deployed LLM assistants, users rarely provide explicit feedback, making it difficult to assess whether the interaction succeeded or failed. We therefore investigate user repair: follow-up user actions such as rephrasing, correcting, clarifying, or requesting more information. Such actions may signal breakdown recovery, but they can also reflect ordinary task progression, making them difficult to interpret in isolation. Using a human-annotated dataset of industrial multi-turn conversations, we examine when repair is informative about assistant-side deficiencies and user dissatisfaction. We find that repair occurs more often after deficient responses, but it is also common after non-deficient responses, making it a weak standalone signal of failure. Its diagnostic value becomes clearer when it is interpreted jointly with assistant response quality, repair type, and dialogue context. Repetition/rephrasing is most strongly associated with negative feedback on deficient responses, whereas requests for more information are more common after non-deficient responses, suggesting a role in collaborative task progression. We further show that repair is not always local: while most repairs target the immediately preceding turn, a substantial minority target earlier dialogue context. These findings position user repair as a context-dependent interactional signal for monitoring, evaluating, and diagnosing deployed assistants.