Multi-Agent Systems
Multi-agent systems coordinate several specialized agents that collaborate to complete a larger task.
Prerequisites
When One Agent Isn't Enough
A single agent with a broad toolset and a long, generic set of instructions can become harder to reason about, test, and secure as its responsibilities grow. A multi-agent system splits that responsibility across several narrower, specialized agents — each with its own focused instructions and tools — coordinated by an orchestration layer that routes work between them.
Incoming Task
Broad ScopeToo large for one narrowly-focused agent.
Coordinator
Routes WorkSplits responsibility across specialized agents.
Research Agent
Narrow SpecialtyIts own focused instructions and tools.
Writing Agent
Narrow SpecialtyBuilds on the research already gathered.
Review Agent
Narrow SpecialtyChecks the work before it counts as done.
Combined Result
Coordinated OutputWhat each specialized agent contributed, together.
Benefits and Costs
One set of instructions and tools
Simpler to build and debug
Can become overloaded for broad tasks
Specialized agents, focused instructions
Easier to secure per agent (least privilege)
Coordination overhead and more failure surfaces
The coordination mechanism itself is agent orchestration — a multi-agent system needs something deciding which agent handles what, and how their outputs combine. This coordination is genuine added complexity, which is why a single well-scoped agent is usually the better starting point until a task clearly spans multiple distinct specialties.
Common Mistakes
Reaching for multi-agent design before it's justified
A single well-scoped agent is simpler to build, debug, and secure — split into multiple agents only once a task genuinely spans distinct specialties.
No clear division of responsibility between agents
Overlapping responsibilities between agents make it unclear which one should handle a given situation, and can produce conflicting outputs.
Treating coordination as an afterthought
Routing tasks between agents and combining their outputs is real design work, covered by agent orchestration, not something that happens automatically.
Giving every agent the same broad tool access
Part of the value of splitting into specialized agents is scoping each one's permissions to what it specifically needs — granting broad access to all of them undoes that benefit.
Underestimating the added failure surface
Each additional agent and coordination step is another place something can go wrong — more agents mean more to monitor and more ways for a task to fail.
Interview Question
When would you use a multi-agent system instead of a single agent, and what does it cost you?
I'd use a multi-agent system once a task genuinely spans multiple distinct specialties that don't fit well within one agent's toolset and instructions — splitting responsibility across focused, specialized agents makes each one simpler to reason about and easier to secure with tightly scoped permissions. The cost is real coordination overhead: something has to route tasks between agents and combine their outputs, which is agent orchestration, and every additional agent is another potential failure surface to monitor. A single well-scoped agent is usually the better starting point — I wouldn't reach for multi-agent design just because it's possible, only once the task clearly justifies the added complexity.
What an interviewer may ask next
- What determines whether a task actually needs multiple agents instead of one?
- How does multi-agent design help with applying least privilege to tool access?
- What new failure modes does a multi-agent system introduce that a single agent doesn't have?
Explain It in 30 Seconds
Multi-agent systems split responsibility across several specialized agents, each with focused instructions and tools, coordinated by an orchestration layer that routes tasks and combines results. The benefit is easier reasoning and tighter permission scoping per agent; the cost is real coordination overhead and more failure surfaces to monitor. A single well-scoped agent is usually the simpler starting point — multi-agent design is worth it once a task genuinely spans distinct specialties.