Chen, Y.; Sun, M.; Tadepally, L.; Wang, J.; Barcenilla, H.; Gonzalez, L.; Brodin, P.

The application of artificial intelligence to biomedical research increasingly depends on iterative cycles in which AI systems analyze experimental data, propose follow-up conditions, and drive automated execution at scale, a paradigm central to Bio-AI and autonomous laboratory science. For such cycles to operate, laboratory protocols must be expressed in a form that is simultaneously human-readable and machine-executable. Natural-language descriptions, the current standard in laboratory practice, do not satisfy this dual requirement. We present Culsma, a formal language and execution framework that elevates laboratory protocols from informal prose to semantically explicit workflow programs that can be analyzed, validated, executed, and transferred across settings. The same protocol can be read and verified by a bench scientist, and parsed, validated, and executed by an automated pipeline without re-translation. We demonstrate an end-to-end implementation providing concrete evidence of practical viability.