将 AI 智能体扎根于生物现实：自主湿实验室的构建模块

Pooja Rao, Research Scientist, Petar Sirkovic, Software Engineer, and Annalisa Pawlosky, Research Scientist, Google We are bringing AI agents to the wet-lab bench at Google Research to navigate complex biological landscapes. By grounding agents in physical lab robotics, we can run fast closed-loop molecular design cycles where agents learn how to learn, ultimately discovering single-digit nanomolar protein binders for cancer targets. 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 20 100 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. First, we present the VERified Infrastructure for Discovery Laboratories (VERID) framework to bridge the gap between the high-level reasoning of AI agents and the complex, error-prone physical reality of a wet lab. It is a semantic digital twin of the laboratory workcell, i.e., a live, stateful 3D volumetric entity tree that tracks everything from robotic arm geometry to individual well volumes, reagent identities, and pipette tip inventory. 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. Through VERID, an AI agent plans, simulates, and executes liquid-handling protocols on a liquid handling robot, combining synchronized multi-camera views with live state tracking to refine experiments across cycles. Built on top of the open-source PyLabRobot framework , VERID provides four core capabilities: Live 3D state tracking: Allow agents to check deck layout, well volumes, and available tips before acting.