Agent-based diffusion models (ABMs) have been increasingly used for making decisions in today's complex and dynamic managerial environment. To be a reliable managerial decision tool, they should be rigorously calibrated against empirical data sets to capture key features of real-world scenarios. However, since that the implementation of ABMs is time-consuming, their efficient calibration is still an open challenge. In this article, we present a calibration framework for a parsimonious ABM by using a simple assistant model (AM) to provide an initial searching point. With a differential equation-based diffusion model (DEM) as the AM, we construct a two-stage calibration procedure for the ABM, in which the first stage is to build an explicit connection between parameter spaces of the ABM and the DEM and obtain an initial searching point, and the second stage is to search for the optimal estimates via an iterative local search method. The case study demonstrates that the proposed framework can identify an optimal solution by evaluating only a few points. It also reveals that some of the ABMs have better explanatory and forecasting performance than the DEM.