Application-specific reasoning mechanisms (ASRMs) development is a rapidly growing domain of systems engineering. A demonstrative implementation of an active recommender system (ARS) was realized to support designing ASRMs and to circumvent procedural obstacles by providing context-sensitive recommendations. The specific problem for the research presented in this paper was the development of a synthetic validation agent (SVA) to simulate the decisional behaviour of designers and to generate data about the usefulness of the recommendations. The fact of the matter is that the need for the SVA was raised by the pandemic, which prevented involving groups of human designers in the recommendation testing process. The reported research had three practical goals: (i) development of the logical fundamentals for the SVA, (ii) computational implementation of the SVA, and (iii) application of the SVA in data generation for the evaluation of usefulness of recommendation. The SVA is based on a probabilistic decisional model that quantifies decisional options according to the assumed decisional tendencies. The three key concepts underlying the SVA are (i) decisional logic, (ii) decisional knowledge, and (iii) decisional probability. These together enable generation of reliable data about the decisional behaviours of human designers concerning the obtained recommendations. The completed tests proved the above assumption.