Coding Agent Evaluation

• Key points are in the visual diagram above.

Understanding Coding Agent Evaluation helps you build reliable, scalable agent applications instead of fragile demos.

Think of Coding Agent Evaluation as a specialized capability in your Coding Agents engineering toolkit.

Scenario

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

Solution

In Coding Agents, apply Coding Agent Evaluation to this scenario: A production team in Coding Agents uses Coding Agent Evaluation 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 Coding Agent Evaluation (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 Coding Agent Evaluation happens in the code.

1# Coding Agent Evaluation — 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 Coding Agent Evaluation8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain coding agent evaluation clearly."},11        {"role": "user", "content": f"What is coding agent evaluation?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Coding Agent Evaluation before production
• No logging or tracing around coding agent evaluation steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.