Multi-Agent Coding Assistant

Shows supervisor pattern, role specialization, and shared state — how real 'AI dev teams' are architected at scale.

Sprint team in one repo: architect writes spec, dev codes, QA breaks it, senior dev reviews — you merge when green.

Scenario

OAuth feature: Planner writes 8-step spec. Implementer adds routes + tests. Tester fails on callback URL. Implementer fixes. Reviewer flags missing state param. Implementer patches. Tester passes. PR opened.

Solution

In Capstone Projects, apply Multi-Agent Coding Assistant to this scenario: OAuth feature: Planner writes 8-step spec. 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 Coding Assistant (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 Coding Assistant happens in the code.

1# Multi-Agent Coding Assistant — 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 Coding Assistant8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain multi-agent coding assistant clearly."},11        {"role": "user", "content": f"What is multi-agent coding assistant?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• All agents with all tools
• No test gate before review
• Unbounded implementer-reviewer loops

Quick recap — the most important points from this module.