Paving the way for AI agents in biology

This post introduces the concept of making biological data infrastructure more agent-friendly, highlighting the challenges of heterogeneous data formats and brittle retrieval mechanisms. It presents a case study using scientific research agents (Claude, Biomni OSS, Edison Analysis, GPT) to retrieve viral sequence data from NCBI Virus, demonstrating that even advanced models struggle with accuracy and reproducibility without deterministic retrieval layers. The development of VirBench, a benchmark for evaluating agent performance in biological data retrieval, is also discussed. The post argues for the necessity of building biological databases with agents as scaled users to enable reliable scientific discovery.