Agent Metrics

• Key points are in the visual diagram above.

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

Think of Agent Metrics as a specialized capability in your Eval Engineering & Observability engineering toolkit.

Scenario

A production team in Eval Engineering & Observability uses Agent Metrics to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Eval Engineering & Observability, apply Agent Metrics to this scenario: A production team in Eval Engineering & Observability uses Agent Metrics 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 Agent Metrics (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 Agent Metrics happens in the code.

1# Agent Metrics — 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 Agent Metrics8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain agent metrics clearly."},11        {"role": "user", "content": f"What is agent metrics?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for Agent Metrics before production
• No logging or tracing around agent metrics steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.