Multi-agent systems are AI architectures made of several specialized agents that each own a narrower part of a task and communicate to coordinate, hand off work, or check one another's output, instead of asking a single generalist agent to plan, act, and verify an entire workflow alone.
Why split one agent into several in the first place?
A single agent holding an entire workflow in one context tends to lose precision as the task grows: instructions for one part bleed into another. Splitting the work lets each agent stay narrow, with its own instructions, tools, and context, closer to how a human team divides labor than one person doing every role at once.
What patterns hold a multi-agent system together?
The most common is the orchestrator-worker pattern: one agent plans and delegates, subagents each handle a piece in isolation, and the orchestrator assembles the results. When agents are built by different organizations rather than living in one codebase, coordination usually moves to a shared protocol like A2A instead.
Is a multi-agent system always the right call?
No. Every additional agent adds a handoff, and every handoff is a place a task can drop context or drift from the goal, so the cost grows with the number of agents. Agentic AI earns a multi-agent architecture on work with genuinely distinct roles, not on a workflow a single, well scoped agent already does reliably.
Frequently asked questions
When does a multi-agent system actually beat a single agent?
When a task naturally splits into distinct roles that benefit from separate context or separate expertise, research plus writing plus fact checking, or when several independent pieces of work can genuinely run in parallel. A single well scoped agent is simpler and usually the right first build.
What is the most common multi-agent pattern?
The orchestrator-worker pattern: one agent plans and delegates, several narrower worker agents each handle a piece, and the orchestrator assembles the results. It shows up under different names in most production multi-agent systems because it keeps coordination in one place.
What is the biggest risk in a multi-agent system?
Coordination overhead outgrowing the benefit. Every extra agent adds handoffs, more model calls, and more places a task can silently drop context between agents, so the failure mode to watch for is a system that costs more and moves slower than one careful generalist agent would have.