5 Patterns for Coordinating Multiple AI Agents
The article discusses five patterns for coordinating multiple AI agents to avoid chaos and ensure function in multi-agent systems.
Why it matters
Coordinating multiple AI agents is crucial as the complexity of AI systems grows, and these patterns provide a framework for building effective and reliable multi-agent systems.
Key Points
- 1Supervisor-Worker Pattern: One agent orchestrates, many agents execute
- 2Pipeline Pattern: Agents form a chain, output of one becomes input to the next
- 3Broadcast-and-Subscribe: One agent publishes events, interested agents consume them
- 4Blackboard Pattern: Agents share a common workspace to read, write, and coordinate
- 5Negotiation Pattern: Agents negotiate to reach a consensus on actions and outcomes
Details
The article outlines five key patterns for coordinating multiple AI agents in a multi-agent system. The Supervisor-Worker pattern has one agent breaking down tasks, spawning specialized worker agents, and synthesizing their outputs. The Pipeline pattern structures agents in a sequential chain, with each performing a specific step. The Broadcast-and-Subscribe pattern decouples agents by having one publish events that others can independently consume. The Blackboard pattern uses a shared workspace for agents to read, write, and coordinate. The Negotiation pattern has agents negotiate to reach consensus on actions and outcomes. These patterns help separate useful multi-agent systems from ones that devolve into chaos or hallucination feedback loops.
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