Agentic AI systems can plan, call tools, and coordinate specialized sub-agents, enabling multi-step scientific workflows that exceed what single-model text generation can reliably deliver. Yet in high-stakes domains such as regulatory science and toxicology, fluent outputs are not sufficient: adoption hinges on traceability, reproducibility, context-of-use validity, and explicit uncertainty communication. This perspective argues that evidence-based medicine and evidence-based toxicology provide