Using an agent to understand agent traces

Been running an agent in production for a few months and logging everything to an MLflow experiment (spans, tool calls, latencies, token counts, errors). The traces are great but querying them meant writing pandas/SQL by hand every time I wanted to answer a question like "which tool calls are timing out most" or "what's my p95 latency on multi-step runs." So I pointed Databricks Genie at the trace tables. Genie turns natural-language questions into SQL over your data, so now I just ask things in plain English: - "Show me the 10 slowest traces this week and which tool dominated the latency" - "What % of runs hit an error, broken down by tool?" - "Average tokens per trace, trending by day" It generates the SQL, runs it against the MLflow trace data which lives in UC, and hands back a table or chart. Setup was basically: Traces already landing in an MLflow experiment (autolog handles most of this) Flatten the trace/span data into queryable tables Create a Genie space over those tables with a bit of context (what a "span" is, what the tool names mean). For a quick start you can also use genie code directly. Biggest win is that non-SQL folks on the team can now interrogate agent behavior themselves instead of pinging me.