multi-agent-patterns

You can coordinate multiple LLMs by designing a multi-agent architecture with explicit coordination protocols and context isolation strategies to distribute workloads across specialized instances.

Distribute subtasks by implementing supervisor, swarm, or hierarchical orchestration patterns that delegate specialized subtasks to available agents and aggregate their results.

You need context isolation in a multi-agent workflow when managing context per sub-task to reduce context drift, improve reliability, and prevent overlapping information across specialized agents.

Yes, centralized orchestration allows a supervisor to delegate subtasks like search, analysis, and synthesis to domain-specific specialists, monitor progress, and aggregate the final results.

Decentralized orchestration supports parallel reasoning by allowing autonomous agents to coordinate without a central supervisor, distributing workload across specialized language model instances.

Context drift occurs when a single agent loses focus over extended context; multiple agents prevent it by applying explicit protocols to isolate context strictly per sub-task.