Swarm Orchestration

Multi-agent swarm orchestration distributes tasks across agents using parallel or pipeline execution modes. Initialize agents with a topology pattern (mesh, hierarchical, ring, or star), then submit tasks to the orchestrator, which handles automatic task distribution, inter-agent communication, and load balancing across your swarm.

Swarm orchestration supports four core topology patterns: mesh (all agents connected), hierarchical (parent-child relationships), ring (agents in a loop), and star (central coordinator). Choose based on your communication needs, latency sensitivity, and fault tolerance requirements for distributed task coordination.

Memory coordination synchronizes state and context across distributed agents, enabling them to share decisions and outcomes. The orchestrator manages memory consistency during parallel execution, ensuring agents can access shared data and coordinate on complex workflows without race conditions or data conflicts.

Yes, swarm orchestration provides fault tolerance through automatic retries and fallback mechanisms. If an agent fails or a task times out, the orchestrator can reassign work to healthy agents, reroute through alternative paths in the topology, and recover distributed workflows without manual intervention.

Monitoring integrates with agentic-flow hooks for real-time observability. You can track task distribution, inter-agent communication, memory state changes, and topology events. This visibility helps debug distributed workflows, identify bottlenecks, and verify that adaptive strategies are responding correctly.

Use swarm orchestration when tasks require parallel execution, dynamic workload distribution, or resilience across multiple agents. It's essential for scaling beyond single-agent limits, implementing complex multi-step workflows, and building systems that need automatic load balancing and fault tolerance in distributed environments.