A recent study in Nature marks a conceptual advance in medical genetics by reframing rare disease diagnosis as a coordinated reasoning task rather than a static prediction problem. The proposed agentic system orchestrates phenotype, genotype, and knowledge retrieval modules under a large language model host, generating ranked hypotheses with transparent, evidence linked logic. It is training light and dynamically updatable, addressing data scarcity and evolving gene disease knowledge. While clinical impact remains to be proven, the work signals a broader shift from black box accuracy toward interpretable, modular AI that mirrors expert diagnostic reasoning. Computational innovation is increasingly shaping everyday genetic practice. (https://www.nature.com/articles/s41586-025-10097-9 )