Test Execution

• Key points are in the visual diagram above.

Understanding Test Execution helps you build reliable, scalable agent applications instead of fragile demos.

Think of Test Execution as a specialized capability in your Coding Agents engineering toolkit.

Scenario

A production team in Coding Agents uses Test Execution to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Coding Agents, apply Test Execution to this scenario: A production team in Coding Agents uses Test Execution to handle a real user request — reducing manual work and improving response quality with proper validation and logging. 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 Test Execution (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 Test Execution happens in the code.

1# Test Execution — 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 Test Execution8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain test execution clearly."},11        {"role": "user", "content": f"What is test execution?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Test Execution before production
• No logging or tracing around test execution steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.