AI systems are designed as single-agent or multi-agent architectures, each with its strengths and weaknesses. Single-agent systems handle tasks independently and are ideal for simple, well-defined applications where efficiency is key. However, they can struggle with complex tasks and adapting to changing environments. In contrast, multi-agent systems divide tasks among specialized agents, allowing for parallel processing and greater flexibility. They are better suited for handling complex, dynamic environments but require more coordination and computational resources. The choice between single-agent and multi-agent architectures depends on the system's needs now and how it is expected to evolve. Effective communication and monitoring are essential in AI systems, especially when deciding between these two approaches.