Multi-Agent System

What is a multi-agent system?

A multi-agent system contains two or more AI agents that coordinate, collaborate, or divide responsibility toward a goal. Each agent may have its own role, instructions, context, tools, state, and execution loop.

The agents may work sequentially, in parallel, through peer-to-peer communication, or under a supervisor or orchestrator.

How multi-agent systems work

A research system might use one agent to gather sources, another to verify claims, and a third to synthesize the report. An orchestrator can assign work, track dependencies, handle failures, and combine results.

Agents need a communication and handoff design. This may include:

• bounded task descriptions;
• shared or selectively transferred context;
• result formats and quality checks;
• ownership of decisions and state;
• retry, escalation, and stopping rules.

Parallel execution is useful only when tasks are genuinely independent.

Multi-agent system versus single capable agent

A single agent can reason, plan, use multiple tools, and complete complex multi-step work. Multiple roles do not automatically require multiple agents.

A multi-agent design is most useful when specialization, isolation, independent review, separate permissions, or parallelism produces clear value. Otherwise, additional agents can increase cost and coordination problems.

Multi-agent system versus workflow

A workflow defines steps or a graph of execution. A multi-agent system defines multiple agentic participants. A workflow can call one agent repeatedly, while a multi-agent system can operate within a workflow.

Multiple model calls also do not automatically create a multi-agent system. The key distinction is separate agent responsibilities and decision loops, not call count.

Common failure modes

Agents can duplicate work, lose context during handoffs, disagree about ownership, or pass unsupported conclusions to one another. Shared state can become inconsistent, while excessive shared context can erase the benefits of specialization.

Reliable systems define contracts, trace each result to its source, set budgets, and make one component responsible for final integration. Security boundaries must apply across agent-to-agent communication.

Why it matters

Multi-agent systems can combine specialized capabilities and perform independent work in parallel. They can also turn one difficult problem into a harder coordination problem.

The design question is not “How many agents can we use?” but “Which responsibilities truly benefit from separate agents?” See [Single-Agent vs Multi-Agent Systems](/single-agent-vs-multi-agent-systems/) for detailed decision rules.