multi-agent-patterns

Multi-agent architecture patterns include supervisor/orchestrator, peer-to-peer/swarm, and hierarchical models. These patterns coordinate distributed AI agents to handle complex task decomposition and parallelization.

You coordinate multiple AI agents using a supervisor or orchestrator model to delegate specialized tasks. This enables parallel execution and efficient context management for complex workflows.

Context isolation in multi-agent systems manages context windows across individual agents to prevent token overload. It ensures agents only process relevant information, optimizing token economics and response accuracy.

The best way to build a multi-agent research system is using a supervisor architecture to coordinate specialized agents like web scrapers and data analysts. This ensures accurate information synthesis and efficient context management.

Consensus protocols in peer-to-peer AI swarms use voting and debate mechanisms to validate outputs and mitigate failure modes. This ensures coordinated decision-making and system reliability across distributed agents.

Choose a hierarchical multi-agent architecture over a peer-to-peer swarm when tasks require structured delegation and top-down coordination. Use peer-to-peer models when agents need equal participation in consensus and debate protocols.