Sandboxed Execution

• Key points are in the visual diagram above.

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

Think of Sandboxed Execution as a specialized capability in your Agent Runtime & Production engineering toolkit.

Scenario

A production team in Agent Runtime & Production uses Sandboxed Execution to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Agent Runtime & Production, apply Sandboxed Execution to this scenario: A production team in Agent Runtime & Production uses Sandboxed 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 Sandboxed 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 Sandboxed Execution happens in the code.

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

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

Quick recap — the most important points from this module.