Multi-Agent Systems
Architectures where multiple specialized AI agents collaborate, debate, or divide tasks.
Definition
Multi-agent systems (MAS) are architectures in which two or more specialized AI agents work together to solve problems that would be difficult or inefficient for a single agent. Each agent typically has a distinct role, set of tools, or domain expertise, and they coordinate through structured communication protocols to accomplish complex objectives.
Key characteristics of multi-agent systems include:
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Role Specialization: Each agent focuses on a narrow responsibility, such as code generation, testing, review, or project management, leading to higher-quality outputs within its domain.
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Communication Protocols: Agents exchange messages, share intermediate results, or debate solutions using defined interaction patterns such as round-robin, hierarchical delegation, or broadcast.
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Emergent Problem-Solving: The collaboration between agents can produce solutions that no individual agent would reach alone, similar to how cross-functional teams outperform individuals.
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Flexible Topologies: Systems can be organized as pipelines, hierarchies, peer networks, or competitive structures where agents critique each other's work.
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Fault Tolerance: If one agent fails or produces poor output, other agents can compensate, retry, or escalate, improving overall system reliability.
Frameworks like AutoGen, CrewAI, and LangGraph provide abstractions for building multi-agent systems with configurable coordination strategies.