Multi-Agent System
Multiple AI agents working together, each with specialized capabilities, to solve complex problems more effectively than a single agent.
Overview
Multi-agent systems distribute intelligence across specialized agents that collaborate to achieve common goals. Each agent has specific expertise - one might analyze URLs, another examines email headers, while another assesses social engineering tactics. These agents communicate, share findings, and build on each other's work. This approach mirrors how human security teams operate, with specialists in different areas working together. Multi-agent systems are more robust, scalable, and capable of handling complex scenarios than monolithic AI systems. They can also continue functioning if one agent fails, providing resilience.
Real-World Examples
- ▸Coordinated threat analysis with specialized detection agents
- ▸Distributed security monitoring across network segments
- ▸Collaborative incident investigation systems
Related Terms
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