Definition: A multi-agent system is a design in which several AI agents, each with its own role, tools and instructions, coordinate to complete a task that would be hard for a single agent to handle reliably.
Each agent specializes: one might plan, another research, another write code or check results. A coordinator, sometimes called a supervisor or orchestrator, routes work between them and decides when the task is done. Agents pass messages or shared state, and each one only gets the tools and data its role needs.
Splitting a large task into roles keeps each prompt focused, limits what any one agent can touch and makes failures easier to locate. The trade-offs are higher cost and latency, more moving parts, and the need for careful observability so engineers can see which agent did what.
Common patterns include a planner with worker agents, a writer paired with a reviewer agent, and pipelines where each agent handles one stage of a document or ticket. Many teams start with a single AI agent and split it only when evaluation shows one agent cannot keep up with the scope.
Ryz AI agent developers and AI pod teams design, test and ship multi-agent systems in your stack, working on your team.
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