AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
A multi-agent system splits a complex job across several specialized AI agents that coordinate, such as one researching and another writing. It can handle workflows too broad for a single agent, but every added agent adds cost and points of failure. It is worth it only when the task genuinely has distinct roles.
A multi-agent system is a project team rather than a solo employee. Each agent has a specialist role. They hand off work, check each other's output, and combine into a result no single agent could produce.
A content production system uses one agent to research, one to outline, one to write, and one to fact-check. The hand-offs are automatic. A human reviews the final draft, not every intermediate step.
When a task is genuinely too complex or too large for a single agent, or when parallel processing would significantly cut the time. Keep it simple until the simpler design breaks.
Do not use multi-agent by default. Each added agent adds failure points, latency, and cost. A single well-designed agent handles most tasks more reliably than a premature team.
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