Agentic AI is not just another tool in the scientific toolkit, but a paradigm shift: allowing autonomous systems not only to collect and process data, but also to hypothesize, experiment and even make decisions in a way independently, agentic AI could fundamentally change the way we approach biology.
The staggering complexity of biological systems
To understand why agentic AI is so promising, we first need to address the scale of the challenge. Biological systems, especially human ones, are incredibly complex – layered, dynamic and interdependent. Take the immune system, for example. It simultaneously operates at many levels, from individual molecules to whole organs, adapting and responding to internal and external stimuli in real time.
Traditional research approaches, while powerful, struggle to account for this vast complexity. The problem lies in the volume and interconnection of biological data. The immune system alone involves interactions between millions of cells, proteins and signaling pathways, each influencing the other in real time. Making sense of this tangled web is almost insurmountable for human researchers.
Enter AI agents: How can they help?
This is where agentic AI comes in. Unlike traditional machine learning models, which require large amounts of curated data and are typically designed to perform specific and narrow tasks, agent AI systems can ingest unstructured and diverse datasets from multiple sources and can operate autonomously with a more general approach.
Beyond that, AI agents are not bound by conventional scientific thinking. They can connect disparate domains and test seemingly unlikely hypotheses that can reveal new insights. What could initially appear as a series of counterintuitive experiments could help to discover patterns or hidden mechanisms, generating new knowledge that can form the foundation for developments in areas such as drug discovery, immunology or precision medicine .
These experiments are performed at unprecedented speed and scale through fully automated, robotic labs where AI agents perform processes in a continuous, 24-hour-a-day workflow. These laboratories, equipped with advanced automation technology, can handle everything from ordering reagents, preparing biological samples, to performing high-throughput screenings. In particular, the use of patient-derived organoids – 3D miniaturized versions of organs and tissues – allows AI-driven experiments to more closely mimic the real-world conditions of human biology. This integration of AI agents and robotic laboratories enables large-scale exploration of complex biological systems, and has the potential to rapidly accelerate the pace of discovery.
From agentic AI to AGI
As AI agent systems become more sophisticated, some researchers believe they could pave the way for artificial general intelligence (AGI) in biology. While AGI—machines with the capacity for general intelligence equivalent to humans—remains a distant goal in the broader AI community, biology may be one of the first fields to approach this limit.
For what? Because understanding biological systems requires exactly the kind of flexible, goal-directed thinking that defines AGI. Biology is full of uncertainty, dynamic systems and open problems. If we build AI that can autonomously navigate this space—making decisions, learning from failure, and proposing innovative solutions—we may be building AGI specifically suited for the life sciences.