How is a team of agents different from one agent with a large tool belt?

**A multi-agent system** runs two or more separate LLM-driven agents that work together on a task. Each agent has its own model call, its own prompt and persona, and its own set of tools. They coordinate by sending messages to each other, not by sharing one big prompt. The word that trips people up is multi. It means multiple agents, not multiple tools. A single agent that has access to twenty tools is still one agent: there is one LLM context per turn, one persona, one decision-maker. Add a second separate LLM context with its own role and you now have two agents. The most common multi-agent setups use role specialization. One agent plans, another researches, another writes, another reviews. Each plays a focused role with a tighter prompt and a more relevant tool set than a single jack of all trades agent could. Coordination happens through message passing managed by the runtime. Three frameworks dominate in 2026. CrewAI models a crew of role-playing agents (planner, researcher, writer). AutoGen models agents as participants in a multi-party conversation. LangGraph models the coordination as a typed state graph where nodes can be different agents. All three implement the same fundamental idea: multiple separate LLM contexts, coordinated by a runtime, talking through messages.

**A multi-agent system** runs two or more separate LLM-driven agents that coordinate, typically with distinct roles, prompts, and tool sets. They communicate by passing messages, not by sharing one prompt. Each agent has its own model context and decision boundary. The definitional line is the agent count, not the tool count. A single agent with a rich tool registry is still one agent: one LLM call per turn, one persona, one context window. The minute you spin up a second LLM call with its own role description and its own state, you have a multi-agent system. This is structural, not a matter of perceived intelligence. Multi-agent designs trade complexity for specialization. Each agent gets a tighter prompt and a more focused tool registry, which often produces better behavior than a single jack of all trades agent. Common patterns include planner-executor (one agent decomposes the task, another runs the steps), researcher-writer (one gathers, one synthesizes), and debate (two agents argue, a third decides). The cost is coordination overhead: more model calls, more orchestration logic, more places for the system to deadlock or loop. Three production frameworks dominate. **CrewAI** models a crew of role-playing agents with built-in delegation and shared memory. **AutoGen** treats agents as participants in a multi-party conversation, with a group-chat manager driving turn order. **LangGraph** lets each node in a state graph be a different agent, with explicit edges describing coordination patterns. All three implement the same fundamental pattern: separate LLM contexts coordinated by a runtime through message passing.