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Most business process automation is still developed using traditionalautomation technologies such as workflow engines. These systems provide domainspecific languages that require both business knowledge and programming skillsto effectively use. As such, business users often lack adequate programmingskills to fully leverage these code oriented environments. We propose aparadigm for the construction of business automations using natural language.The approach applies a large language model to translate business rules andautomations described in natural language, into a domain specific languageinterpretable by a business rule engine. We compare the performance of variouslanguage model configurations, across various target domains, and explore theuse of constrained decoding to ensure syntactically correct generation ofoutput.

A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language