Researchers at the NIH have developed GeneAgent, an artificial intelligence agent based on large language models that improves the accuracy of gene set analysis by autonomously verifying its functional annotations against expert-curated biological databases. This approach mitigates common AI pitfalls like hallucinations and circular reasoning, reinforcing genetic functional interpretation without human intervention. Published in Nature Methods, GeneAgent demonstrated strong performance validating gene function claims on over 1,100 gene sets, validated by expert reviewers, advancing genomic data interpretation capabilities.