Swarm Pattern

Supervisor bottlenecks limit scale. Swarms let agents self-organize: a triage agent hands off to a specialist, who hands off to a validator. OpenAI's Swarm and similar frameworks formalize this handoff model.

A swarm is a relay race — each runner does their leg and passes the baton to the next specialist, without a coach micromanaging every step.

Scenario

Triage agent receives question → realizes it's a billing issue → hands off to Billing Agent → Billing Agent resolves → hands off to Satisfaction Agent for follow-up.

Solution

In Agent Design Patterns, apply Swarm Pattern to this scenario: Triage agent receives question → realizes it's a billing issue → hands off to Billing Agent → Billing Agent resolves → hands off to Satisfaction Agent for follow-up. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Swarm Pattern (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Swarm Pattern happens in the code.

1from swarm import Swarm, Agent  # import dependencies2
3triage = Agent(name="Triage", instructions="Route or answer.", functions=[transfer_to_billing])4billing = Agent(name="Billing", instructions="Handle refunds.", functions=[transfer_to_triage])5
6client = Swarm()  # key line for Swarm Pattern7response = client.run(agent=triage, messages=[{"role": "user", "content": "I want a refund"}])

• Treating Swarm Pattern as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for swarm pattern

Quick recap — the most important points from this module.