The article presents an approach to synthesizing artificial intelligence agents (AI agents), in particular, control and decision support systems for process operators in various industries. Such a system contains an identifier in the feedback loop that generates digital predictive associative search models of the Just-in-Time Learning (JITL) type. It is demonstrated that the system can simultaneously solve (outside the control loop) two additional tasks: online operator pre-training and mutual adaptation of the operator and the system based on real-world production data. Solving the latter task is crucial for teaching the operator and the system collaborative handling of abnormal situations. AI agents improve control efficiency through self-learning, personalized operator support, and intelligent interface. Stabilization of process variables and minimization of deviations from optimal conditions make it possible to operate process plants close to constraints with sustainable product qualities. Along with higher yield of target product(s), this reduces equipment wear and tear, utilities consumption and associated harmful emissions. This is the key merit of Model Predictive Control (MPC) systems, which justify their application. JITL-type models proposed in the article are more precise than conventional ones used in MPC; therefore, they enable the operation even closer to process constraints. Altogether, this further improves the reliability of production systems and contributes to their sustainable development.