What Is a Multi-Agent AI System? Architecture and Use Cases

Why Multiple Agents?

Single agents face practical limits: context windows fill up on long tasks, one model can't be expert at everything, and sequential single-threaded work is slow.

Multi-agent systems address this by: splitting work across specialised agents, running tasks in parallel, using different models for different subtasks, and having oversight agents review work.

Common Multi-Agent Patterns

1. Supervisor + Workers

One orchestrator agent breaks down the task and delegates to specialised worker agents. Workers report back, supervisor synthesises. Good for complex research or analysis tasks.

2. Pipeline (Sequential)

Agent A's output becomes Agent B's input. Good for document processing: extract → analyse → summarise → format.

3. Debate/Review

One agent generates output, another critiques it, a third synthesises. Reduces hallucinations on high-stakes tasks.

4. Parallel Workers

Multiple agents work on independent subtasks simultaneously. Good for researching multiple topics in parallel.

When NOT to Use Multi-Agent

Multi-agent systems are more complex, harder to debug, and more expensive. Don't reach for them unless:

• The task is too long for a single agent's context window
• Parallelisation provides meaningful speed improvement
• Different subtasks require genuinely different expertise or tools

Design AI Agent Systems

Our workshop covers single and multi-agent design patterns with hands-on implementation exercises.