Swarm Intelligence

Some problems benefit from parallel diverse attempts rather than one planner. Swarms explore solution spaces faster and avoid single-point-of-failure in reasoning.

A swarm is a brainstorming room with 20 people throwing ideas on sticky notes — the best ideas emerge from volume and cross-pollination, not one manager's plan.

Scenario

Five copywriter agents each draft a headline; ranking agent scores against CTR predictors; top 2 go to A/B test.

Solution

In Multi-Agent Systems, apply Swarm Intelligence to this scenario: Five copywriter agents each draft a headline; ranking agent scores against CTR predictors; top 2 go to A/B test. 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 Intelligence (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 Intelligence happens in the code.

1from swarm import Swarm, Agent  # OpenAI educational Swarm2
3client = Swarm()  # key line for Swarm Intelligence4agents = [Agent(name=f"writer_{i}", instructions=f"Style variant {i}") for i in range(5)]5# Each generates; supervisor merges best

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

Quick recap — the most important points from this module.