Designing Agentic AI for Pharma & Life Sciences with Graph Technology: From Complex Evidence to Explainable Insights

Agentic AI in the pharmaceutical and life sciences sectors leverages graph technology to transition from fragmented data and isolated language model experiments to scalable, explainable, and production-ready AI systems. This approach addresses challenges such as data heterogeneity, connectivity, and unstructured content by using knowledge graphs, which align with the inherently networked nature of biology. A core principle discussed is the separation of reasoning and action, where large language models (LLMs) handle intent and context, while deterministic tools execute actions, enhancing reliability and traceability. The use of schema-first knowledge graph construction ensures that AI systems are grounded, with clear domain schemas guiding data extraction and linking extracted facts to their sources. This architecture, already implemented by companies like Novartis, supports various stages of the drug lifecycle, including target identification and safety monitoring, by providing a structured context for intelligent reasoning and a memory layer for enterprise knowledge. The focus is on creating intelligent, explainable systems that are integrated across research and operational dashboards, facilitating deeper insights and better decision-making in regulated environments.