Microbially induced carbonate precipitation (MICP) is a biogeochemical process that uses microbes to create favorable conditions in a porous medium for bio-cementation. The hydrolysis of urea and the resulting variation in pore-scale chemistry creates variations in MICP. Research is needed to characterize the spatial and temporal development of precipitation at the particle level that influences soil properties and mechanical behavior due to varying granular characteristics. Several researchers have used reactive transport modeling to predict precipitation distribution patterns over meter-length scales; however, this continuum approach is less applicable for assessing varying granular characteristics at the pore-scale. This study investigates agent-based modeling (ABM) as a new framework to simulate complex interactions at the micro-level to generate phenomena observed at the macro-scale. In this study, an ABM was developed to simulate urea hydrolysis by simplifying the complex biogeochemical interactions between the bacteria Sporosarcina pasteurii and an aqueous MICP environment, including the pH-dependent ammonium speciation. Results show that the ABM captures similar urea degradation trends compared to existing reactive transport models and experimental data. Additionally, insights from the ABM suggest that nutrient depletion zones emerge during ureolysis and are influenced by super-agent size and distribution. These findings indicate that ABM can provide additional insights into the MICP process that cannot be captured by reactive transport modeling.