A/B tests take weeks to run. What if real-data-driven AI personas could predict the outcome in advance? Title: Data-Driven Persona-Conditioned Agents for A/B Test Simulation URL: ❓ What question format works best for AI personas? 💡 Pairwise evaluation, where the agent directly compares variants, wins clearly: 0.75 accuracy on CTR and 0.80 on subscription tests. Independent scoring drops to around 0.40. ❓ Do you need proprietary data to build good personas? 💡 Open e-commerce data matched proprietary data almost as well. But out-of-domain data (movie reviews) hurt accuracy, showing domain match really matters. ❓ Is it better to go deep on behavior data or wide on demographic diversity? 💡 A "deep pool" of highly engaged users won for CTR prediction, but for subscription tests, a demographically diverse pool performed just as well. ❓ Can you cut costs by using fewer personas? 💡 Yes — subsampling from 935 down to 500 personas kept accuracy nearly unchanged, roughly halving simulation cost. #ABTesting# #LLMAgents#