Agentic AI is moving from experimental tools toward workflows that can perform meaningful work across life sciences operations. In GxP environments, that shift raises a more difficult question than what AI can do: What should it be allowed to do, under what conditions, and what evidence is needed to trust the result?
This session will explore how life sciences organizations can introduce agentic workflows, without treating autonomy as the ultimate goal. The discussion will examine how to define bounded tasks, ground agents in controlled information, preserve traceability, evaluate performance, handle uncertainty, and keep human oversight proportionate to risk. It will also consider how an agent’s scope can expand as it demonstrates reliable performance within a governed workflow.
Using examples from R&D, quality, and manufacturing, the speakers will discuss where agents can reduce manual data gathering, evidence reconstruction, and repetitive analysis; where scientific judgment remains essential; and how teams can assess readiness across their data, processes, technology, and governance.
Attendees will leave with advice from veteran executives and a practical framework for deploying and evaluating agentic AI in GxP environments, establishing the controls required for an initial use case, and building trust through demonstrated performance rather than promises of autonomy.