How an AI-Native Laboratory Turns an Experimental Goal Into Physical Operations

An AI-native laboratory is designed so that computational planning and physical experimentation share a common operating layer. A research goal is decomposed into tasks, translated into a protocol, checked against available resources and then executed by automated equipment.

1. Define the goal and constraints

The system needs a measurable objective, available samples, safety limits and stopping rules. A broad prompt is not enough for responsible execution.

2. Build the protocol

Planning agents can retrieve literature, select reagents and arrange steps. The proposed protocol still needs validation against local equipment, contamination controls and biological assumptions.

3. Translate steps into instrument calls

A software abstraction describes what each device can do. The planner combines these capabilities while an execution layer checks code, reserves instruments and enforces timing constraints.

4. Observe and adapt

Measurements return to the planning layer. The system may repeat a step, change a parameter or stop, but adaptation should occur within predefined bounds and leave an auditable record.

5. Keep humans accountable

People replenish consumables, maintain hardware, investigate unexpected failures and decide whether results support a scientific conclusion. Automation can reduce routine handling without transferring scientific responsibility to the machine.

What to evaluate

Useful benchmarks include intervention time, failure recovery, reproducibility across laboratories, contamination rates, cost per completed workflow and whether automated choices preserve experimental controls.