Swarm Orchestration

You coordinate distributed agent swarms by configuring a topology, spawning agents, and triggering parallel task orchestration. This enables automatic task distribution and coordinated workflows across dynamic mesh, hierarchical, or adaptive topologies.

Scalable AI workflows can use mesh, hierarchical, or adaptive topologies. Mesh allows peer-to-peer inter-agent communication, hierarchical centralizes control, and adaptive topologies dynamically shift based on load and fault tolerance requirements.

Multi-agent orchestration supports automatic load balancing and fault tolerance to ensure robust distributed execution. These mechanisms prevent single-point failures and distribute workloads evenly across active agents in the swarm.

Agents in a distributed swarm communicate and share memory through built-in coordination mechanisms integrated with the agent-control-plane. This shared memory enables seamless collaboration and state synchronization during complex automation tasks.

The best way to manage complex automation tasks with collaborating agents is initializing a swarm orchestration session. You configure the topology, spawn the required agents, and trigger parallel workflows to execute the project collaboratively.

You can integrate hooks into the agent-control-plane during swarm orchestration. This allows you to extend inter-agent communication, inject custom logic into shared memory coordination, and adapt fault tolerance behaviors dynamically.