Sound Agentic Science Requires Adversarial Experiments

LLM-based agents are increasingly used in scientific data analysis, but risk generating plausible analyses optimized for publishable positive results. This paper proposes that non-experimental claims produced with agentic assistance be evaluated under a falsification framework to ensure scientific rigor.