Agent Orchestration

Simple Definition

Agent orchestration is the process of coordinating multiple AI agents so they work together effectively. An orchestrator manages which agent handles which task, what context each agent receives, and how their outputs feed into each other.

Think of it like a project manager who assigns tasks to specialists: the orchestrator doesn’t do the work itself. It makes sure the right agents get the right information at the right time.

Why You Need Orchestration

A single AI agent can only do so much. Complex tasks benefit from having specialized agents:

• One agent for research
• Another for writing
• Another for fact-checking
• Another for formatting output

Without orchestration, these agents would work in isolation. With orchestration, their outputs connect into a coherent workflow.

How Agent Orchestration Works

1. Goal received: the orchestrator gets a task
2. Task breakdown: the goal is split into sub-tasks
3. Agent assignment: each sub-task goes to the right specialized agent
4. Execution and monitoring: the orchestrator tracks progress and handles failures
5. Result synthesis: outputs from all agents are combined into the final result

Orchestration Patterns

• Sequential: agents run one after another (agent A’s output feeds agent B)
• Parallel: multiple agents run simultaneously on different parts of a task
• Hierarchical: a manager agent delegates to worker agents
• Dynamic: the orchestrator decides which agent to call based on results

Related Terms

• AI Orchestration, the broader concept including single-model pipelines
• Multi-Agent System, the kind of system agent orchestration manages
• Agentic Workflow, workflows that agent orchestration enables
• AI Agent, the individual components being orchestrated