Long-Running Agents

• Key points are in the visual diagram above.

Understanding Long-Running Agents helps you build reliable, scalable agent applications instead of fragile demos.

Think of Long-Running Agents as a specialized capability in your Agent Runtime & Production engineering toolkit.

Scenario

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

Solution

In Agent Runtime & Production, apply Long-Running Agents to this scenario: A production team in Agent Runtime & Production uses Long-Running Agents 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 Long-Running Agents (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 Long-Running Agents happens in the code.

1# Long-Running Agents — 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 Long-Running Agents8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain long-running agents clearly."},11        {"role": "user", "content": f"What is long-running agents?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Long-Running Agents before production
• No logging or tracing around long running agents steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.