Swarm Orchestration

Multi-agent swarms coordinate distributed AI workflows by configuring mesh, hierarchical, or adaptive topologies to manage parallel, pipeline, and adaptive task execution across multiple agents.

Multi-agent systems handle load balancing and faults through shared memory coordination and resiliency features, ensuring distributed tasks continue executing even when individual agents fail.

Initialize a swarm topology and spawn agents by running the agentic-flow hooks swarm-init command to establish the network structure and begin coordinated parallel tasks.

Adaptive orchestration flows support integration with Hooks pre-task and post-task lifecycle, allowing you to inject custom logic before and after agents execute distributed operations.

Mesh topologies enable peer-to-peer agent coordination, hierarchical topologies structure agents in command tiers, and adaptive topologies automatically distribute tasks based on dynamic load.

Parallel execution in multi-agent orchestration is not suitable when tasks require strict sequential dependencies, as pipeline and adaptive modes better handle ordered operations across the distributed system.