Decentralized coordination of multi-agent communication networks underpins autonomous UAV swarms, sensor meshes, and cyber-physical systems that must operate without a single point of failure, yet quantitative design rules linking topology degradation to coordination collapse are still lacking. This paper aims to characterize the fault tolerance of a discrete linear consensus protocol that drives mobile agents’ scalar states toward a common value over a time-varying communication graph. The convergence rate and fault tolerance budget of the protocol are explicitly tied to the algebraic connectivity ${\lambda }_2$ of the graph Laplacian, and three failure regimes—random independent failures ($M_1$), sequential staged failures ($M_2$), and targeted attacks on the highest-degree hubs ($M_3$)—are analyzed using a full-factorial Monte-Carlo experiment of 1620 runs implemented in Python with the Mesa framework and NetworkX. Critical failure thresholds ${\phi }_c$ are 0.42–0.51 for $M_1$, 0.38–0.48 for $M_2$, and 0.28–0.38 for $M_3$; a Kruskal–Wallis test (p < 0.001) confirms the ordering ${\phi }_c^{M^3}<{\phi }_c^{M^2}<{\phi }_c^{M^1}$, providing quantitative design rules for fault-tolerant consensus-driven networks.