Cost & Token Budgets

• Key points are in the visual diagram above.

Understanding Cost & Token Budgets helps you build reliable, scalable agent applications instead of fragile demos.

Think of Cost & Token Budgets as a specialized capability in your Agent Runtime & Production engineering toolkit.

Scenario

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

Solution

In Agent Runtime & Production, apply Cost & Token Budgets to this scenario: A production team in Agent Runtime & Production uses Cost & Token Budgets 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 Cost & Token Budgets (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 Cost & Token Budgets happens in the code.

1# Cost & Token Budgets — 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 Cost & Token Budgets8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain cost & token budgets clearly."},11        {"role": "user", "content": f"What is cost & token budgets?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Cost & Token Budgets before production
• No logging or tracing around cost budgets steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.