Beyond One-shot: AI Agents for Learning in Field Experiments
Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design. Significant barriers exist to extracting actionable knowledge from prior experimental data to inform new interventions. We study whether tool-augmented agentic AI can automatically learn from experimental data to generate new interventions in subsequent experiments. Through two-stage field experiments in healthcare prescription messaging (693,139 patient visits), we compare a Human + Chatbot method (Stage 1: behavioral experts with conversational AI co-designing 13 message variants, 444,691 patient visits) against a Tool-Augmented A...
| Authors | Junjie Luo, Ritu Agarwal, Gordon Gao |
|---|---|
| Primary category | cs.AI |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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