This article explores how the performance of language model-based agent systems can be quantitatively analyzed. It identifies key scaling laws and coordination strategies through experiments with various agent architectures, revealing insights on tool coordination, capability saturation, and error amplification. The findings help predict optimal coordination strategies for different tasks.