Event-Driven Agents

• Key points are in the visual diagram above.

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

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

Scenario

A production team in Agent Runtime & Production uses Event-Driven 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 Event-Driven Agents to this scenario: A production team in Agent Runtime & Production uses Event-Driven 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 Event-Driven 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 Event-Driven Agents happens in the code.

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

• Skipping evaluation for Event-Driven Agents before production
• No logging or tracing around event driven agents steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.