What is multi-agent orchestration?
Multi-agent orchestration runs one planner agent that delegates sub-tasks to executor agents, each scoped to a narrower job, then folds their outputs back into the original goal. Frameworks like OpenAI Swarm, CrewAI, LangGraph, and Gumloop ship different shapes of this pattern: handoffs, hierarchical crews, state graphs, and invoke_agent calls.
What is multi-agent orchestration?
Multi-agent orchestration is the pattern where one agent decomposes a task and dispatches sub-tasks to other agents, then merges their results. The dispatching agent (often called orchestrator, planner, or supervisor) holds the goal; executor agents own narrower scopes like search, code edits, or CRM lookups. Anthropic calls this orchestrator-workers: a central LLM breaks down work, delegates to worker LLMs, and synthesizes outputs.
How frameworks implement it
Frameworks pick different coordination shapes. OpenAI Swarm uses handoffs, where one agent returns another agent from a tool call and the runtime swaps the active system prompt. CrewAI exposes sequential, hierarchical, and hybrid processes inside a Crew. LangGraph models the dispatch as a state graph with conditional edges. Gumloop ships an invoke_agent tool that lets a parent spin up subagents concurrently and read each transcript back.
The durability gotcha
The non-obvious failure mode is durability. A planner that runs for hours waiting on executor outputs dies on a server restart unless state is persisted between steps. Durable Swarm wraps OpenAI Swarm's loop in a Postgres-backed durable workflow for exactly this reason. Without it, teams end up routing agent messages through SQS or Kafka to survive restarts.
For execs
One dispatching agent plus three executor agents absorbs work that previously needed a small pod. The shift is concurrency: sub-tasks run in parallel instead of one analyst working sequentially.
Last updated: May 20, 2026