Grounding AI agents in biological reality: Building blocks for autonomous wet labs

Frontier AI systems have the potential to transform biology research and drug discovery by intelligently navigating vast combinatorial search spaces (e.g., a single 100-amino-acid protein spans at least 20¹⁰⁰ possible sequences) calling for dynamic multi-candidate exploration. Our long-term research vision is that AI agents will not only ideate and analyze, but also run lab experiments to test their hypotheses, learn from the results, and self-improve to solve complex problems — all driven by human scientific agency.

Realizing this vision requires solving two intertwined challenges: First, frontier models still do not reason reliably about 3-dimensional geometry and physical state — which poses a problem in the automation of the specialized liquid-handling robots that move reagents, track volumes, and execute molecular biology protocols in a wet lab. Second, while agentic hypothesis generation and in-silico design are advancing rapidly, needing repeated cycles of real-world feedback to validate the proposals and navigate vast search spaces, real-world wet-lab experiments have not kept pace, making experimental readouts more of a bottleneck than ever.

In this post we share three research projects highlighting our progress on bridging the gap between in-silico AI exploration and real-world wet-lab experimentation:

• Enabling AI agents to perform safe, resource efficient experimentation grounded in physical measurements by using the VERID framework.
• Demonstrating closed-loop agentic scientific discoveries on our prototypes for aptamer and de-novo protein design.

Grounding agents in physical reality

The safety of a command depends on the live physical context as well as the code. An agent can generate syntactically valid code that is physically impossible (e.g., lowering an 8-channel pipette into a narrow trough that is too short), logistically invalid (e.g., aspirating from a depleted well), or scientifically compromised by unmodeled fluidic effects.