Single-agent or multi-agent? Choosing the right architecture for your AI system

AI systems developers face a critical decision when determining whether to use single-agent or multi-agent architectures, each with distinct trade-offs in performance, cost, and complexity. Single-agent systems consolidate reasoning, memory, and tool execution within one AI instance, making them suitable for straightforward workflows with low task complexity, where debugging and latency sensitivity are essential considerations. In contrast, multi-agent systems distribute tasks across specialized agents, which require explicit coordination mechanisms and are ideal for scenarios involving hard security boundaries, multi-domain scaling, or organizational separation. While multi-agent systems can improve task completion rates and offer better cost management, they introduce significant coordination overhead that can hinder scalability if not architected carefully. Regardless of the chosen architecture, both require robust infrastructure capable of supporting autonomous decision-making, goal-directed behavior, environmental interaction, and adaptive behavior, with Redis offering sub-millisecond latency and semantic search capabilities to meet these demands.