Automating and Scaling Behavioral Scientific Research on AI Agents

2026-08-09

AI Summary

This work addresses the labor-intensive and non-scalable nature of traditional approaches to studying AI agent behavior by introducing AEROBAT, the first multi-agent system that fully automates the behavioral science research pipeline. AEROBAT integrates hypothesis generation, controlled experimental design, large-scale simulation, and statistical analysis into a unified framework. The system evaluated 79 hypotheses across 12 target behaviors through 1,240 experiments and 23,512 simulation rounds, yielding moderate to strong statistical evidence for 26 hypotheses. This approach substantially enhances the scale, efficiency, and reproducibility of behavioral studies in artificial intelligence and enables the discovery of novel behavioral patterns.

Abstract

As AI agents are increasingly deployed in complex environments, understanding their behaviors becomes critical. Yet behavioral scientific research on AI agents remains manual and labor-intensive. We introduce AEROBAT, the first multi-agent system to automate behavioral scientific research on AI agents. Given an arbitrary target behavior by its user, AEROBAT automatically executes a full pipeline of behavioral scientific research---generating hypotheses about the behavior, designing and executing controlled experiments, making behavioral assessments, analyzing the results, and writing reports. For 12 target behaviors, we used AEROBAT to generate and test 79 hypotheses: designing 1,240 controlled experiments and executing 23,512 simulation rounds in total. Moderate-to-strong statistical evidence was found for 26 hypotheses, including some novel ones. In sum, our results demonstrate that automated behavioral scientific research on AI agents can complement and extend the reach of manual research.

Problem

• AI agents
• behavioral scientific research
• automation
• scaling
• hypothesis testing

Innovation

• automated behavioral research
• multi-agent system
• AI agent behavior
• hypothesis generation
• controlled experimentation