Inputs
The systems and standard your team chooses
- Runbook, source material, and definition of done
- Records from Snowflake, Databricks, Tableau
Process optimization
Parameter tuning & yield lift
What it does
- Recommends recipe parameter changes that lift yield
- Quantifies the expected gain and the risk of each change
- Tracks whether the change held after deployment
How it works
- 1Models yield against process parameters
- 2Ranks candidate adjustments by expected lift
- 3Monitors the post-change cohort
Works with
Deployment contract
What goes in, what comes out, and where people review.
Outputs
A finished artifact with its work attached
- Recommends recipe parameter changes that lift yield
- Quantifies the expected gain and the risk of each change
Approvals
Human control at the steps that matter
Choose which steps run automatically and which stop for a named reviewer before deployment.
Evals
Quality measured against your rubric
Score completed runs against accepted examples and task-specific criteria. Review failures with the source trace before changing the runbook or model.
Example result
The result is reviewable, not a black box.
A completed run keeps the task, sources, actions, approvals, and eval result together. Open any step to inspect what happened or send it back with a correction.
RUN
Process optimization
- Models yield against process parametersSource trace saved
- Ranks candidate adjustments by expected liftSource trace saved
- Monitors the post-change cohortOutput prepared
Rubric score, sources, and action log travel with the result
Related agents
Deploy in days, not months.
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