The field needs a way to decipher this incredible complexity. Could the rise of agentic AI, artificial intelligence capable of autonomous decision-making and action, be the key to breaking through this impasse?
Agentic AI is not just another tool in the scientific toolkit but a paradigm shift: by allowing autonomous systems to not only collect and process data but also to independently hypothesize, experiment, and even make decisions, agentic AI could fundamentally change how we approach biology.
To understand why agentic AI holds so much promise, we first need to grapple with the scale of the challenge. Biological systems, particularly human ones, are incredibly complex -- layered, dynamic, and interdependent. Take the immune system, for example. It simultaneously operates across multiple levels, from individual molecules to entire 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 sheer volume and interconnectedness 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.
This is where agentic AI steps in. Unlike traditional machine learning models, which require vast amounts of curated data and are typically designed to perform specific, narrow tasks, agentic AI systems can ingest unstructured and diverse datasets from multiple sources and can operate autonomously with a more generalist approach.
Beyond this, AI agents are unbound by conventional scientific thinking. They can connect disparate domains and test seemingly improbable hypotheses that may reveal novel insights. What might initially appear as a counterintuitive series of experiments could help uncover hidden patterns or mechanisms, generating new knowledge that can form the foundation for breakthroughs in areas like drug discovery, immunology, or precision medicine.
These experiments are executed at unprecedented speed and scale through robotic, fully automated laboratories, where AI agents conduct trials in a continuous, round-the-clock workflow. These labs, equipped with advanced automation technologies, can handle everything from ordering reagents, preparing biological samples, to conducting high-throughput screenings. In particular, the use of patient-derived organoids -- 3D miniaturized versions of organs and tissues -- enables AI-driven experiments to more closely mimic the real-world conditions of human biology. This integration of agentic AI and robotic labs allows for large-scale exploration of complex biological systems, and has the potential to rapidly accelerate the pace of discovery.
As agentic AI 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 threshold.
Why? Because understanding biological systems demands exactly the kind of flexible, goal-directed thinking that defines AGI. Biology is full of uncertainty, dynamic systems, and open-ended problems. If we build AI that can autonomously navigate this space -- making decisions, learning from failure, and proposing innovative solutions -- we might be building AGI specifically tailored to the life sciences.
