Grounding AI Agents in Biological Reality: Building Blocks for Autonomous Wet Labs

Frontier AI models excel at generating hypotheses and designing molecules in silico, but they struggle to reason reliably about three-dimensional geometry and physical state. This gap creates a bottleneck in biology research: while agents can propose thousands of candidate sequences, validating them requires repeated cycles of real-world wet-lab experiments. Google Research has developed a set of building blocks to bridge this divide, enabling AI agents to navigate complex biological landscapes by grounding their reasoning in physical lab robotics.

The core innovation is VERID (VERified Infrastructure for Discovery Laboratories), a semantic digital twin of the laboratory workcell. VERID tracks everything from robotic arm geometry to individual well volumes and reagent identities in real time. Before an agent sends a command to a liquid-handling robot, it simulates the action within this digital twin. This prevents physically impossible actions, such as lowering a pipette into a trough that is too short, or logistically invalid ones, like aspirating from a depleted well. The system provides live 3D state tracking, fast virtual dry runs, hardware safety checks, and synchronized video logs, allowing agents to refine experiments across multiple cycles without wasting reagents or damaging equipment.

With this safety infrastructure in place, researchers demonstrated closed-loop agentic scientific discovery using DNA aptamers. Aptamers are single-stranded DNA molecules that bind to specific target proteins. In a series of three design-test-iteration cycles targeting myoglobin and α-thrombin, AI agents designed candidate sequences, which were screened on magnetic beads and sequenced to return quantitative data. The agents learned from the wet-lab readouts to overcome failure modes. For example, when initial designs for myoglobin resulted in non-specific "sticky" binders due to G-quadruplex motifs, the agents autonomously revised their design rules in the next round, capping promiscuous motifs and pivoting to different structural scaffolds. This adaptive process raised confirmed on-target hit rates to 33.5% for myoglobin and 42.1% for α-thrombin.