Researchers have introduced the EMBL AI Librarian, a specialized knowledge layer designed to optimize life-sciences information retrieval for AI agents. While existing databases like Europe PMC contain over 40 million indexed records, they are traditionally optimized for human keyword syntax, forcing agents to perform iterative searches and parse entire documents to extract specific evidence. The EMBL AI Librarian overcomes this by utilizing a single Large Language Model (LLM) to orchestrate the retrieval process. The LLM decomposes natural language queries into strategic subqueries for the Europe PMC engine, then reads the retrieved papers to extract direct, relevant evidence. Performance evaluations across multiple benchmarks demonstrate significant improvements over existing methods. On the ScholarQABench, the system achieved a Citation F1 score increase of over 16 points compared to recent strong baselines. In open-domain question answering tasks using the LitQA2 benchmark, a GPT-5.4 agent grounded in the Librarian knowledge layer outperformed web-search-based agents by approximately 8 points. Furthermore, the tool enhanced agreement with expert consensus when integrated into claim-verification pipelines. By shifting the burden of syntax and document parsing from the agent to a dedicated orchestration layer, the EMBL AI Librarian facilitates more efficient and accurate downstream biological tasks, including sequence manipulation and protocol inquiries.