Agent-based models (ABMs) are computational simulations with autonomous agents interacting within an environment, offering a distinct scientific approach alongside inductive and deductive inference. In contrast to inductive inference, which identifies patterns from data, and deductive inference, which tests the consequences of assumptions, ABMs blend these methods. They commence with assumptions akin to deduction but don’t generate theorems. Instead, ABMs produce simulated data based on a set of rules, allowing inductive analysis. Especially valuable for complex systems, ABMs excel in modeling non-aggregate, emergent behaviors not derived from basic interactions. ABMs present advantages over other models by being more realistic, accommodating complex relationships and heterogeneous populations in diverse environments. Their flexibility allows integration of various aspects, from social to environmental factors. Originating in the 1970s, large-scale ABMs gained prominence in the 1990s with increased computing power. They find applications in diverse domains, such as human migrations, climate change effects on civilizations, disease propagation, plant root colonization, cell alterations’ impact on multicellular behavior, economics, and transport modeling. Despite their potential, ABMs face challenges like justifying model inputs and dealing with large, complex outputs. This chapter advocates for Julia language and the Agents.jl framework for ABM due to their efficiency in model construction, execution, and analysis, emphasizing the importance of suitable tools for gaining knowledge effectively from ABMs.