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Multi-Agent Systems (MAS)
Multi-Agent Systems coordinate multiple specialized AI agents to collaboratively perform complex, large-scale tasks, enabling higher-value automation across business operations.
What it is
- A framework where multiple AI agents, each with a specific role or expertise, work together to achieve a common goal.
- Enables division of labor, parallel processing, and specialized problem-solving within a unified system.
How it works
- Agents communicate and delegate tasks based on their capabilities and the current state of the workflow.
- A central orchestrator or protocol (e.g., MCP) manages agent interactions, data sharing, and synchronization.
- Agents can be deployed for end-to-end automation of processes like software development, healthcare diagnostics, or insurance claims.
Trade-offs
- Adds complexity in design, coordination, and failure handling compared to single-agent systems.
- Requires robust inter-agent communication protocols and error recovery mechanisms.
When to use it
- For complex, cross-functional workflows that exceed the capabilities of a single agent.
- When scalability, specialization, or parallel processing are required to handle large or diverse tasks.
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