Multi-Agent Orchestration Tools
Frameworks like CrewAI and LangGraph provide the routing and delegation logic that coordinates several specialized agents.
Prerequisites
Overview
Coordinating several agents — deciding which one handles a subtask, passing results between them, and knowing when the overall task is done — is exactly the kind of routing logic multi-agent orchestration tools exist to handle.
Where It Fits
Incoming Task
Orchestrator
Routes subtasksSpecialized Agents
e.g. a research agent, a writing agent.
Combined Result
Key Points
- Delegation
- An orchestrator decides which specialized agent should handle a given subtask, based on role or capability.
- Result aggregation
- Outputs from multiple agents need to be combined coherently, which is its own non-trivial step.
- Failure isolation
- One agent failing shouldn’t necessarily fail the whole task — orchestration logic needs a policy for partial failure.
Interview Question
What real problem does a multi-agent orchestration tool solve that a single, larger agent doesn’t?
Splitting a task across specialized agents — a researcher, a writer, a reviewer — can produce better results than one generalist agent, but only if something coordinates delegation, passes results between them, and combines their outputs. Orchestration tools provide that routing and aggregation logic rather than leaving it to ad hoc code.
Explain It in 30 Seconds
Multi-agent orchestration tools like CrewAI and LangGraph handle delegating subtasks to specialized agents, passing results between them, and combining their outputs — the coordination logic a multi-agent system needs beyond just running several agents independently.
Real-World Stack
Technologies commonly used to implement this in production.