Classifying agent trace queries with Jev and Luna
A team building enterprise agent trace intelligence uses Jev and Luna to classify the queries agents generate so they know which query patterns to optimize for.
We've been working on building trace intelligence across all your company sessions. We're building dimensions of trace data especially relevant to the offices of a CTO, CISO, and CFO. You'll be able to answer things like "how often do files in your codebase show up in agent read operations and how much does it cost" (an example visual below) This is a fun but gnarly data engineering challenge. But IMO the real story is all the software we are creating along the way. We're building internal tools to evaluate schema choices, we use data from our agent to evaluate the types of queries to optimize for (using Jev and Luna to classify), we build visual profiling tools test query performance throughout. Making software now is so much more fun and interesting. Anyways, I can't wait to show this stuff off. Your agent trace data contains multitudes and you need many ways to look at it. Also, if this is interesting enough that you might want to run a pilot, please DM!