Generative AI changed how researchers interact with data. Scientists can summarize clinical studies, analyze research papers, and explore complex datasets in seconds.
But real pharmaceutical workflows are more complex than answering prompts. Drug discovery involves multiple steps such as analyzing evidence, validating safety data, coordinating experiments, and generating reports. Traditional GenAI tools can assist, but they cannot execute these workflows end to end.
This is where Agentic AI comes in.
Instead of isolated AI tools, organizations are exploring systems of specialized AI agents that can reason, act, and collaborate. One emerging concept is the Agent Factory, a platform designed to build, manage, and govern multiple AI agents working together across the drug discovery pipeline.
Rather than just generating insights, these agents can help plan tasks, retrieve data, validate results, and support decision making while keeping scientists in the loop.
The future of AI in pharma may not be just better models. It may be better systems that allow those models to actually do meaningful work.