What is the core idea behind multi-agent systems?
Single agents execute. Multi-agents coordinate.
How do multi-agent systems differ from related concepts?
| Concept | Difference |
|---|---|
| Single Agent vs Multi-Agent | One system vs multiple interacting systems |
| Multi-Agent vs Workflow | Workflows are fixed. Agents can adapt dynamically |
| Multi-Agent vs LLM | LLM is a model. Multi-agent is a system design |
How do multi-agent systems work?
- Multiple agents are assigned roles
- Agents communicate or share state
- Tasks are divided or coordinated
- Outputs are combined into final results
What are the limitations of multi-agent systems?
- Coordination complexity
- Error propagation between agents
- Increased latency and cost
Why are multi-agent systems important?
Multi-agent systems enable complex problem-solving, automation, and scalable AI workflows that single models cannot handle alone.
How are multi-agent systems used in practice?
Used in research agents, autonomous workflows, simulations, and enterprise automation. Frameworks include AutoGen (Microsoft), CrewAI, and LangGraph.
Frequently Asked Questions
Why use multiple agents instead of one model?
Specialization improves performance and allows complex tasks to be decomposed into manageable sub-tasks.
Are multi-agent systems more powerful?
They can be, but they are also more complex and harder to manage.