Swarm Orchestration

You coordinate distributed AI workflows by applying multi-agent orchestration across mesh, hierarchical, and adaptive topologies to achieve scalable, fault-tolerant task execution. This enables parallel, pipeline, and memory-sharing coordination for AI experiments.

Structuring multi-agent coordination involves selecting mesh, hierarchical, or adaptive topologies. You define custom agent roles like coder, tester, and reviewer to handle real-time task distribution and load balancing across the swarm.

Yes, building fault-tolerant AI swarms requires agentic-flow v1.5.11+ and Node.js 18+. These dependencies support the underlying load balancing, memory sharing, and fault tolerance mechanisms for distributed agents.

Yes, you can integrate hooks for pre-task and post-task synchronization in AI experiments. This allows you to manage adaptive orchestration systems and coordinate memory sharing across multiple agents during task execution.

Load balancing and fault tolerance in a multi-agent swarm work through real-time task distribution across adaptive topologies. The system coordinates custom agent roles and shares memory to ensure scalable execution continues if individual agents fail.

Use mesh topology for decentralized parallel coordination or hierarchical topology for structured, multi-level task delegation in distributed AI agents. Adaptive topologies dynamically shift between these patterns based on real-time load balancing needs.