Swarm Orchestration

Multi-agent swarms coordinate parallel tasks by utilizing mesh, hierarchical, and adaptive topologies with automatic task distribution and load balancing. This architecture enables scalable AI workflows and maintains progress through fault tolerance in distributed environments.

Implementing mesh and hierarchical topologies for distributed AI workflows involves orchestrating a multi-agent swarm with dynamic topology support. This method provides automatic task distribution and fault handling, maintaining execution progress across distributed environments.

Yes, you can use agentic-flow pre-task and post-task hooks to achieve memory coordination and task orchestration. These hooks facilitate synchronization and coordination across agents within the distributed swarm during parallel and pipeline execution.

You start orchestrating a multi-agent swarm by configuring your desired topology, spawning the required agents, and initializing the task orchestration. This setup enables immediate automatic task distribution and load balancing across the swarm.

Yes, multi-agent orchestration supports fault tolerance for distributed execution by utilizing dynamic topologies and fault handling mechanisms. This ensures that the distributed swarm maintains progress and continues operations even when individual agents fail.