---
title: "OpenAI Swarm Agentic AI Design Patterns"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# OpenAI Swarm Agentic AI Design Patterns

[Swarm Repo](https://github.com/openai/swarm)

[Swarm: The Agentic Framework from OpenAI](https://composio.dev/blog/swarm-the-agentic-framework-from-openai/)

[Watch: Swarm from Open AI - routines, handoffs and agents explained with code](https://www.youtube.com/watch?v=mTE-VLVh63w)

OpenAI’s experimental Swarm framework is meant more as an educational tool than a production-ready system—but it introduces several key design patterns for orchestrating multi-agent systems. In Swarm, agents aren’t monolithic; instead, they’re designed with specialized roles and communicate through clearly defined patterns. Here are some of the core design patterns:

**1. Handoffs:**  
A central idea is the “handoff” pattern. When an agent encounters a task that falls outside its expertise, it uses a special tool call to delegate (or “handoff”) that part of the task to another agent better suited for the job. This pattern lets the system seamlessly transfer control between specialized agents, maintaining a fluid conversation or workflow. 

[Handoff Discussion](https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/design-patterns/handoffs.html)


**2. Routines:**  
Agents often follow predefined “routines”—structured sequences of steps or instructions that guide their behavior. These routines help agents to manage multi-step tasks and ensure consistency across interactions. This makes it easier for developers to compose complex workflows out of simple, repeatable steps.  

[Swarm from OpenAI - Routines, Handoffs, and Agents explained (with code)](https://www.ai-bites.net/swarm-from-openai-routines-handoffs-and-agents-explained-with-code/)


**3. Agent Specialization:**  
Instead of having one agent try to do everything, Swarm promotes the idea of specialized agents (e.g., triage, sales, support). Each agent is designed with specific instructions and tool sets tailored to its domain. This specialization enables more efficient problem-solving and clearer delegation of tasks.

**4. Delegation & Sequential Workflows:**  
Building on handoffs, agents can operate in a sequential workflow where one agent completes its part of a task and then delegates the next step. This pattern helps break down complex tasks into manageable, discrete actions while preserving overall context.

**5. Event-Driven Communication (Pub/Sub):**  
Underlying these patterns is often a publish/subscribe (pub/sub) architecture, where agents exchange messages or events. This event-driven approach decouples agents, allowing them to operate asynchronously and maintain state externally, which is especially useful given Swarm’s stateless design.

Together, these patterns provide a modular and flexible approach to designing multi-agent systems—offering a glimpse into how future AI systems might coordinate complex, real-world tasks. Although Swarm itself is experimental and educational, these design patterns have inspired more robust frameworks aimed at production-level applications.
