Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation
A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering effort, and weeks of wall-clock time. Can AI agents---conditioned on behavioral profiles and contextual descriptions of the intervention---simulate outcomes accurately enough to vet candidate treatments before committing live traffic? We formalize this question as a \emph{Simulated Randomized Controlled Trial} (S-RCT) and derive a two-layer error decomposition that separates agent approximation error from subsampling error, enabling targeted improvements to each. The framework is agent-agnostic: any behavioral model---from a fine-tuned spec...
| Authors | Stefan Hut, Lorenzo Masoero |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 11 September 2026 |
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