What Is a Multi-Agent System?
A multi-agent system is a collection of AI agents that collaborate to complete a complex task. Each agent specialises in a specific domain, and together they can achieve goals that a single agent cannot handle effectively.
Single Agent vs Multi-Agent
A single agent is like a generalist employee who does everything: research, analysis, writing, and delivery. For simple tasks this is fine. For complex tasks it creates problems:
- The agent's context fills up with information from many different domains
- Quality suffers because one model cannot be expert at everything simultaneously
- A failure in one step can cascade through the entire task
- It is hard to audit what went wrong
A multi-agent system is like a team of specialists:
- A Research Agent gathers information
- An Analysis Agent processes the data
- A Writing Agent produces the output
- A Review Agent checks quality before delivery
- A Coordinator Agent manages the overall workflow
Each agent gets a clean, focused context. Each can be optimised for its specific role. Failures are isolated and easier to identify.
When to Use Multi-Agent
- Complex tasks that require multiple distinct skills or knowledge domains
- Tasks that can be broken into parallel workstreams to reduce completion time
- Tasks requiring quality control — a reviewer agent checks the output of a worker agent
- Tasks with high stakes — multiple agents cross-checking each other reduces errors
- Long-running tasks where different phases require different capabilities
Key Takeaways
- A multi-agent system is a collection of AI agents that collaborate to complete a complex task.
- Complex tasks that require multiple distinct skills or knowledge domains
- Tasks that can be broken into parallel workstreams to reduce completion time
- Tasks requiring quality control — a reviewer agent checks the output of a worker agent