Run more experiments with less overhead
Someone launches a test. After a week, they check the numbers. The variant is slightly ahead. They call it and ship. Nobody noticed the confidence interval still spans zero.
How the Experimentation works
The agent calculates required sample sizes before tests launch, monitors traffic allocation during the run, and halts decisions until statistical thresholds are met. Results get documented with confidence levels, not just directional observations.
Experiment management features:
- Calculates minimum sample size based on baseline metrics and detectable effect
- Monitors traffic split and alerts on allocation drift
- Reports confidence intervals and p-values in real time
- Archives experiment results with hypotheses, metrics, and learnings
Why you need the Experimentation
Organizations running five or more experiments per quarter who need to trust their results. Teams running occasional tests may not need the infrastructure. Teams where false positives create costly mistakes benefit most.
How the Experimentation compares
The Data Science Agent supports model building and feature engineering. The Experimentation Agent focuses specifically on controlled tests and statistical validity. Product teams run experiments. Data science teams build models.
