Multi-Agent System
Multi-Agent Systems (MAS) represent a paradigm shift in AI development where multiple specialized AI agents collaborate to solve complex tasks. This approach has gained traction with the emergence of frameworks like LangGraph, AutoGen, llama-agents and CrewAI.
See also: Agent-to-Agent (A2A) Communication, which explores how agents interact and coordinate within multi-agent systems.
Key Concepts
- Specialized Roles: Each agent can be optimized for specific tasks
- Collaborative Problem-Solving: Agents work together to tackle complex challenges
- State Management: Shared context and memory across agent interactions
- Flexible Architecture: Support for various communication patterns and workflows
- Human-in-the-Loop: Integration of human feedback and oversight
- Scalable Design: Ability to add or modify agents as needed
Recommendations
For simpler applications, single-agent solutions are often appropriate and easier to maintain. We recommend to maximize and optimize the "single agent" first.
When the complexity of the application exceeds the capabilities of a single agent, a Multi-Agent System can be a more effective approach. For example, a single agent might struggle with very complex instructions or may not always pick the right tool (tool overload) for the job.
Benefits:
- provides intuitive separation of concepts in your architecture
- allows optimzing the individual agents
- allows reuse of agents (composable architecture)
- can improve overall performance and scalability
So consider Multi-Agent Systems when building complex AI applications that require:
- Division of tasks among specialized components
- Sophisticated decision-making processes
- Real-time collaboration between AI agents
- Integration of human oversight
- To avoid "Tool overload" for single agents
Orchestration Pattern
- "Manager"-"Worker" architecture: A manager coordinates the work of multiple connected agents.
- Decentralized architecture: Agents act as "Peers", that may coordinate or pass over tasks to other agents.
Integration
Multi-Agent Systems can be implemented using various frameworks:
- LangGraph for graph-based workflows
- AutoGen for dynamic conversations
- CrewAI for role-based task delegation
- llama-agents
- n8n