Multi-Agent System

Why One Agent Isn't Always Enough

A single AI agent works well for tasks that fit within a manageable conversation. But some goals are too large, too complex, or require too many specialized skills to hand off to one agent.

Imagine asking an agent to "research AI developments from the past month, summarize the top 10 stories, write a newsletter draft, and suggest a distribution strategy." That's four different types of work — research, synthesis, writing, and marketing — that might all benefit from separate handling.

Multi-agent systems solve this by breaking work across specialized agents that collaborate.

The Common Pattern: Orchestrator + Workers

Most multi-agent systems use a hub-and-spoke design:

• Orchestrator — a coordinating agent that receives the overall goal, breaks it into subtasks, and delegates to worker agents
• Worker agents — specialized agents that do one thing well: search the web, write code, summarize documents, call an API

The orchestrator collects the workers' outputs and synthesizes them into a final result. It may loop — asking a worker to revise based on another worker's feedback.

A Concrete Example

A research multi-agent system might have:

• A search agent that queries the web for recent papers
• A reader agent that processes and extracts key points from each paper
• A synthesizer agent that combines findings into a coherent summary
• An orchestrator managing the flow between all three

Tradeoffs

Multi-agent systems are more powerful for complex tasks but also more expensive (more LLM calls), slower, and harder to debug. Coordination introduces points of failure — an orchestrator can misinterpret a worker's output or send incomplete context. Start with a single agent and move to multi-agent only when you've hit a real ceiling.