This work introduces the Surrogate Advisory System (SAS), a Python package that aims to reduce the existing complexity when working with surrogate-based MDAO systems. SAS automates most of the steps for a CPACS-based MDAO workflow and drastically reduces required efforts to integrate surrogate models within MDAO systems. Key developments enabling this automation, such as a newly developed Python interface with the Process Integration and Design Optimization tool RCE, a comprehensive surrogate modelling engine and a design database structure are discussed. Furthermore, an advisory framework providing insights into the inherent trade-off between computational efficiency and accuracy when working with surrogate models is presented. It is shown that a-priori estimation of a surrogate model's accuracy for a given number of samples is difficult, but that an estimation of the potential surrogate model candidates in a provided workflow can be made.