Intent-based automation engineering method for automation of a production process, comprising the steps: receiving, by a device, comprising a control unit and model unit, an intent model, correlating to process intent, comprising production process functions, constraints on measurable properties on the production process functions and/or production process function sequences required for the production process; wherein the process intent relates to desired states to achieve in the production process, undesired states to avoid in the production process, properties of the production process input to achieve and/or properties of the production process input to achieve, wherein the intent model comprises automation engineering data, which are recorded using an intent-language, wherein the intent-language formulates a quantifiable expectation on the behavior of one or more components of the production process; receiving, by the device, a process model, correlating to process knowledge comprising a production process behaviour, wherein the production process behaviour comprises a physical behaviour relating to a representation of laws of nature and/or a digital behaviour relating to designed software logic, wherein the process model is determined by abstracting the process knowledge, wherein the abstracting comprises converting individual machine-readable expert statements on process knowledge into the process model, wherein the process knowledge is extracted from existing specifications using pattern recognition algorithms, natural language processing and/or AI-based classification; determining, by the model unit, a machine-readable production model linking the received intent model to the received process model, wherein the production model comprises data for maintaining and monitoring a steady state of the production process, data for maintaining and monitoring a particular state of the production process, data for maximizing stability of a control of the production process and/or data for understanding dynamic aspects of the production process; determining, by the control unit, a control strategy for controlling the production process dependent on the provided production model, wherein the control strategy comprises an optimization strategy for the production process; and operating, by the control unit, the production process based on the determined control strategy, wherein the production process comprises a physical production process, relating to a physical material transformation, a digital production process, relating to firmware tests, and/or a mixed production process, relating to a calibration process between the physical production process and the digital production process. receiving, by a device, comprising a control unit and model unit, an intent model, correlating to process intent, comprising production process functions, constraints on measurable properties on the production process functions and/or production process function sequences required for the production process; wherein the process intent relates to desired states to achieve in the production process, undesired states to avoid in the production process, properties of the production process input to achieve and/or properties of the production process input to achieve, wherein the intent model comprises automation engineering data, which are recorded using an intent-language, wherein the intent-language formulates a quantifiable expectation on the behavior of one or more components of the production process; receiving, by the device, a process model, correlating to process knowledge comprising a production process behaviour, wherein the production process behaviour comprises a physical behaviour relating to a representation of laws of nature and/or a digital behaviour relating to designed software logic, wherein the process model is determined by abstracting the process knowledge, wherein the abstracting comprises converting individual machine-readable expert statements on process knowledge into the process model, wherein the process knowledge is extracted from existing specifications using pattern recognition algorithms, natural language processing and/or AI-based classification; determining, by the model unit, a machine-readable production model linking the received intent model to the received process model, wherein the production model comprises data for maintaining and monitoring a steady state of the production process, data for maintaining and monitoring a particular state of the production process, data for maximizing stability of a control of the production process and/or data for understanding dynamic aspects of the production process; determining, by the control unit, a control strategy for controlling the production process dependent on the provided production model, wherein the control strategy comprises an optimization strategy for the production process; and operating, by the control unit, the production process based on the determined control strategy, wherein the production process comprises a physical production process, relating to a physical material transformation, a digital production process, relating to firmware tests, and/or a mixed production process, relating to a calibration process between the physical production process and the digital production process.