Graph orchestration pattern
A multi-agent coordination style that runs tasks as a dependency graph — ordered stages and handoffs — rather than a free-for-all parallel swarm.
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When to use it
Use the graph orchestration pattern when a simple parallel execution of tasks doesn't suffice. This pattern coordinates tasks in a dependency graph, enabling structured execution of complex workflows like multi-stage data processing or automated prospecting.
Quick example
In Amazon Bedrock, orchestrating a multi-agent system for prospecting requires tasks to be executed in a specific order. You configure a graph orchestration pattern to manage dependencies and ensure each task completes before the next begins. Here, the graph orchestration pattern is the framework that sequences the tasks for efficiency and accuracy.
start → task A → task B → task C → end
Ecosystem
Graph orchestration patterns are part of a structured execution flow, coordinating with agents and tasks within a system.
┌─ agent A ─┐
start →│ graph orchestration │→ end
└─ agent B ─┘
Misconceptions
| Misconception | Rebuttal |
|---|---|
| It's just parallel execution | It sequences tasks based on dependencies |
| Any task graph is orchestration | Orchestration requires managing task handoffs |
Trade-offs
- Task clarity — requires detailed dependency mapping
- Controlled execution — can be slower than parallel processing
- Error isolation — complex to debug if dependencies are misconfigured