Single Agent vs Multi-Agent: When to Build a Multi-Agent System

AI agents use a large language model (LLM) to reason, plan, and interact with tools to accomplish tasks. The core components of an agent are the LLM, tools for external actions like web searches, and memory for retaining context. The ReAct (Reasoning + Acting) workflow allows an agent to reason about a task, call necessary tools, observe the output, and repeat until it can generate a final answer. A single-agent design is sufficient for simple tasks, but complex workflows benefit from a multi-agent system where specialized agents handle distinct roles under the coordination of an orchestrator. While multi-agent systems offer modularity for tasks like research or coding, they also introduce greater latency, cost, and maintenance complexity.