Multi-Agent Design

• Key points are in the visual diagram above.

Interviewers probe when to use one agent vs many, how agents share state, and how you prevent infinite debate or duplicated work.

An orchestra: each musician (agent) has a part; the conductor (supervisor) coordinates timing — not everyone playing the same note.

Scenario

Design research report system: Researcher agent gathers sources, Analyst compares findings, Writer drafts, Critic reviews — supervisor routes until Critic approves or max rounds hit.

Solution

In Interview & System Design, apply Multi-Agent Design to this scenario: Design research report system: Researcher agent gathers sources, Analyst compares findings, Writer drafts, Critic reviews — supervisor routes until Critic approves or max rounds hit. 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 Multi-Agent Design (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 Multi-Agent Design happens in the code.

1# Multi-Agent Design — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Multi-Agent Design8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain multi-agent design clearly."},11        {"role": "user", "content": f"What is multi-agent design?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Multi-Agent Design as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for multi agent design

Quick recap — the most important points from this module.