Topics

Why it matters

Reading individual traces tells you what went wrong in a single conversation, but it cannot tell you what is going wrong across your product. Topics automate the pattern-finding step by summarizing traces into plain-language statements and clustering them into categories. This horizontal view surfaces trends that no amount of individual log reading would reveal, like a new class of user requests your system handles poorly, or a recurring failure mode that affects a small percentage of every session. Manual log review does not scale because the volume of production traces quickly outpaces what any team can read. Automated clustering lets you monitor your system at the category level, track how topic distributions shift over time, and prioritize the issues that affect the most interactions. When you spot a problematic topic, you can drill into the underlying traces, create a targeted dataset from them, and build scorers to catch that pattern going forward.