JOIN THE EARLY ACCESS PROGRAM
Agent Observability with Behavior Analytics
Kubit labels every span and draws all agent runs as a Sankey, clustered by the shape of their execution paths. Slice it into funnels and cohorts to spot patterns, then find the signals that triggered them. All queried natively in your warehouse.
Upload a trace file to get started
Traces never leave your warehouse
Databricks and Snowflake native
THESE TEAMS ALREADY RUN KUBIT PRODUCT ANALYTICS IN THEIR WAREHOUSE
PROBLEM
Tens of thousands of runs a week, opened one trace at a time. The failures that matter get found by customers first.
The dashboard is green yet the product is broken
Every span returns OK, so quality failures never reach a screen. The customer finds them first.
No way to tell a big problem from a loud one
Every occurrence arrives as its own trace. The fix list gets ordered by who complained, not by what is costing the most.
Agent behavior and business data live apart
The warehouse holds the traces and the revenue tables. Every question that joins the two becomes a data-engineering ticket.
PLATFORM
Query the agent traces already in your warehouse. Nothing to install.
The traces are already in Databricks or Snowflake. Kubit directly query them there. No SDK, no pipeline.
Databricks Native
Analyze MLflow traces and evals in Unity Catalog.
No SDK to install
No pipeline to build
Queries run in your own account
Snowflake Native
Query AI_OBSERVABILITY_EVENTS and GET_AI_EVALUATION_DATA.
Cortex Agents traces, read in place
Evaluation data lands beside the traces
No second copy to secure
Trace File Upload
Same analysis, no warehouse needed.
Try it before connecting anything
Same labels, same clustering, same analytics
Outcome Joins
Joins agent actions to user activity, ab tests and campaigns.
See which paths actually convert
Tie a pattern to a business outcome, not just an error rate
One query, because it is one warehouse
Try it on the traces already in your warehouse.
WHY KUBIT
Label the spans, group the shapes, name the signal, join the outcome.
Every span labeled, every shape drawn
Polaris labels every span, so a failure that returns a clean status still carries a label. Flows then groups the whole population by the shape of the execution path.
Every pattern comes with its signals
Once a pattern is detected, Triggers shows signals you can verify: what changed, what's overrepresented, which label cluster, and what came earlier.
All of it runs where the traces already sit
No SDK, no pipeline. Your warehouse already holds user activity, A/B tests and campaigns, so you can tie what the agent did to the outcomes that followed.
Your traces stay in your warehouse.
Kubit queries your agent traces in place, inside your own Databricks or Snowflake account. There's nothing to copy and nothing new to secure.
Your Account, Your Permissions · Nothing Copied · OTel to Your Warehouse in Q4
QUERY TRACES IN DATABRICKS AND SNOWFLAKE · BIGQUERY AND CLICKHOUSE IN Q4
FAQ
Questions We Often Get
We already have an observability tool. What is Kubit for?
Kubit works on the traces already stored in your warehouse, and standard OTel collection can write them there directly. Try this on your current tool: ask for last week's ten most common execution-path shapes, ranked, with the signals behind each one. Then see what comes back.
Why not just use what is built into Databricks or Snowflake?
Could we not build this ourselves?
What does labeling every span cost us?
Should we trust a cluster label enough to act on it?
It is a crowded space. What is actually different?
Do I have to install another SDK?
Coming Soon in the Roadmap
Be first to see every agent run as one picture.
7 places in the first cohort. Applications close October 26.
THESE TEAMS ALREADY RUN KUBIT PRODUCT ANALYTICS IN THEIR WAREHOUSE