Multi-agent-based simulations (MABS) of electric vehicle (EV) home charging ecosystems generate large, complex, and stochastic time-series datasets that capture interactions between households, grid infrastructure, and energy markets. These interactions can lead to unexpected system-level events, such as transformer overloads or consumer dissatisfaction, that are difficult to detect and explain through static post-processing. This paper presents a modular, Python-based dashboard framework—built using Dash by Plotly—that enables efficient, multi-level exploration and root-cause analysis of emergent behavior in MABS outputs. The system features three coordinated views (System Overview, System Analysis, and Consumer Analysis), each offering high-resolution visualizations such as time-series plots, spatial heatmaps, and agent-specific drill-down tools. A case study simulating full EV adoption with smart charging in a Danish residential network demonstrates how the dashboard supports rapid identification and contextual explanation of anomalies, including clustered transformer overloads and time-dependent charging failures. The framework facilitates actionable insight generation for researchers and distribution system operators, and its architecture is adaptable to other distributed energy resources and complex energy systems.