In this study have we focused on two aspects of multi-agent simulations. The first is based on a finding in recent years, which is that a standalone global path does not always provide adequate multi-agent navigation in crowded scenarios. A global planner that is aware of other agent configurations and thus finds clearer paths is required for optimal navigation. In real life, usually, an agent is only aware of the area close to it. In this study, by taking into account this limitation, we propose a state-machine-based global planner that monitors agents' close domains and, if required, modifies the path for congestion prevention. The second aspect is the coordination of local and global planners. Multi-agent navigation systems require both local steering and global path planning. As a matter of fact, these two systems should work in coordination since the output of one influences the other. A steering technique cannot always guarantee smooth and collision-free navigation while tracking the global planner's path. Therefore, we propose a system that detects and rebuilds coordination between local and global path planners.