multi-agent-patterns

You can isolate context in multi-agent systems using instruction passing, filesystem coordination, and selective context delegation. These strategies minimize data leakage and redundancy during agent handoffs, ensuring each specialized agent receives only the state and instructions needed for its specific subtask.

Consensus mechanisms in multi-agent coordination include weighted voting, debates, and convergence checks. These governance strategies ensure agents reach agreement during complex parallel tasks, providing structured conflict resolution and reliable synthesis for multi-domain research and reporting workflows.

You should choose a swarm pattern for decentralized peer-to-peer coordination and a hierarchical architecture for layered task delegation. Pattern selection depends on your project's need for parallel exploration versus structured top-down control, with handoff protocols guiding the final orchestration strategy.

You can coordinate a multi-agent research team by deploying a three-agent swarm to handle research, analysis, and reporting. This pattern applies to complex projects requiring parallel exploration and synthesis, using defined handoff protocols to pass context between specialized agents.