Collaborative AI agents for autonomous experimental research

The authors present an autonomous research laboratory in which large language model-based agents assess one another's work and can stop or redirect investigations, demonstrated by studying ensembles of quantum spins in diamond. The agents derived the minimum number of calibration settings needed to predict responses to a defined family of microwave pulse sequences, checked the result computationally, and designed experiments to distinguish competing predictions. When repeated reference measurements revealed signal drift, the agents treated the initial result as inconclusive and revised the measurement protocol without relaxing acceptance criteria, and the revised experiment supported the theoretical predictions.