Agent Orchestration Patterns

Orchestration patterns describe how multiple agents coordinate to accomplish a task. While capability patterns define what individual agents can do (reflect, use tools, plan), orchestration patterns define how agents work together — who talks to whom, who decides what, and how results flow between agents.

These eight patterns are drawn from research and production systems across Anthropic, Google, OpenAI, Microsoft, CrewAI, LangGraph, and others. They’re platform-agnostic — the coordination topology is independent of which LLM or framework you use.

How This Section Relates to Others

Three sections in the Playbook cover different lenses on agent systems:

Section	Lens	Question It Answers
Capability Patterns	What agents can do	”What abilities does an agent need?”
Orchestration Patterns (this section)	How agents coordinate	”How do multiple agents work together?”
Workflow Architecture	How to structure LLM calls	”What’s the right architecture for my workflow?”

Capability patterns are about individual agent abilities. Orchestration patterns are about multi-agent coordination. Workflow architecture patterns are about the overall structure of LLM calls in a system — including single-agent systems.

Pattern Catalog

Structured Topologies

Predictable coordination with defined communication paths.

Pattern	Core Idea	Best For
Sequential	Agents execute in linear order; each output feeds the next	Multi-stage pipelines with clear handoff points
Parallel	Multiple agents work simultaneously; results are aggregated	Independent subtasks that benefit from speed or diversity
Router	Classifier inspects input and dispatches to the right specialist	Varied inputs that require different handling

Coordinated Topologies

Dynamic coordination with a central authority or peer-to-peer transfers.

Pattern	Core Idea	Best For
Hierarchical	Coordinator decomposes tasks, delegates to specialists, synthesizes results	Complex tasks requiring dynamic decomposition
Handoff	Responsibility transfers between peer agents as context evolves	Conversational flows where expertise shifts mid-task
Evaluator-Optimizer	Generator + evaluator iterate until quality criteria are met	Output that requires measurable quality improvement

Emergent Topologies

Flexible coordination where behavior emerges from agent interactions.

Pattern	Core Idea	Best For
Group Chat	Agents converse in shared context; a moderator may synthesize	Problems benefiting from debate or multiple perspectives
Decentralized	No master agent; peers coordinate through shared state or messaging	Highly dynamic tasks where no single agent can plan ahead

Selection Guide

Three questions to narrow your choice:

1. Is the task structure known in advance?

• Yes, fixed steps → Structured topology (Sequential, Parallel, or Router)
• Yes, but steps are dynamic → Coordinated topology (Hierarchical, Handoff, or Evaluator-Optimizer)
• No, emergent → Emergent topology (Group Chat or Decentralized)

2. How do agents need to communicate?

• One-directional (output → input) → Sequential or Parallel
• Hub-and-spoke (coordinator ↔ workers) → Router or Hierarchical
• Peer-to-peer (agent ↔ agent) → Handoff, Group Chat, or Decentralized
• Loop (generate → evaluate → revise) → Evaluator-Optimizer

3. What’s the coordination overhead tolerance?

• Minimal → Sequential or Parallel (simplest to implement and debug)
• Moderate → Router, Hierarchical, or Handoff
• High (justified by task complexity) → Group Chat or Decentralized

Decision Tree

Start: Is the task structure known in advance?
│
├── YES, fixed steps
│   ├── Steps are independent → Parallel
│   ├── Steps must happen in order → Sequential
│   └── Input determines which path → Router
│
├── YES, but steps are dynamic
│   ├── One agent should plan and delegate → Hierarchical
│   ├── Agents hand off to each other → Handoff
│   └── Output needs iterative refinement → Evaluator-Optimizer
│
└── NO, emergent
    ├── Agents benefit from debating → Group Chat
    └── No single agent can plan the whole task → Decentralized

Where to Start

Business leaders evaluating architectures? Start with Sequential and Hierarchical — they map most naturally to how human teams work.

Builders designing your first multi-agent system? Start with Sequential — it’s the simplest to implement and debug. Add complexity only when you hit limits.

Exploring advanced coordination? Handoff and Evaluator-Optimizer solve real production problems that simpler patterns can’t handle.

Sources

These patterns are synthesized from multiple authoritative sources:

• Anthropic — Building Effective Agents
• Google Cloud — Choose a Design Pattern for Agentic AI
• Google ADK — Developer’s Guide to Multi-Agent Patterns
• OpenAI — A Practical Guide to Building AI Agents
• OpenAI Agents SDK — Multi-Agent Patterns
• Microsoft Azure — AI Agent Design Patterns
• Andrew Ng — Agentic Design Patterns
• CrewAI — Framework Documentation
• LangGraph — Multi-Agent Documentation

Related

• Agent Capability Patterns — what individual agents can do
• Multi-Agent Collaboration — the capability pattern for agent teamwork
• Workflow Architecture Patterns — how to structure LLM calls in a system
• Agents Overview — the Agents building block
• Agents Resources — recommended reading on agents