Snowflake touts agentic AI wins at AstraZeneca, Zelis

The post frames oncology's five stages — discovery, screening, diagnosis, treatment, and survivorship — as sharing one root cause: no unified data foundation that can reason across them. It uses that framing to argue that agentic AI, defined as systems that plan and execute across multiple steps, is beginning to address bottlenecks like siloed genomic data and manually assembled tumor board briefs.

The reported numbers are the substance. AstraZeneca's shift from six months and 16 engineers to four days and two, and Zelis's move from a migrated data foundation to over 20 production agents built in a day, are presented as evidence that the pattern is moving from experiment toward measurable impact. The post also cites a JAMA Network Open study's figure of over $50 billion to $60 billion spent annually on failed oncology trials, and a large U.S. screening study finding 21.6% more breast cancers identified with an AI-supported workflow.

The post is candid that agentic capabilities on regulated data need governance built in from the start, and it closes by pointing to a separate piece on the National Cancer Institute's enterprise data platform modernization.