---
title: "Tracing runs and monitoring production"
course: "AI103 · Prompting that works"
unit: "Agents, evals, and observability"
lesson: 33 of 35
source: https://open.maestrocollege.edu/prompting-that-works/tracing-runs-and-monitoring-production
publisher: Maestro College
---

# Tracing runs and monitoring production

Lesson 33 of 35 in **Prompting that works** (AI103), unit "Agents, evals, and observability". An open lesson from Maestro College.

## What the learner will be able to do
- Understand tracing as recording each step of an LLM system run (prompts, retrieved context, tool calls, outputs, tokens, latency) so failures can be located.
- Read a trace of a failed multi-step run and identify the step where it went wrong.
- Define what to log and monitor in production, including user feedback, latency, error rates, and token cost per request, and when an alert should fire.

## Teaching notes
Create an illustration of a trace as a timeline of nested steps with tokens and latency shown on each step.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 33 complete: Tracing runs and monitoring production."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Challenge: design an agent with oversight" (lesson 34 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/challenge-design-an-agent-with-oversight.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Building evals beyond benchmarks
Next lesson: Challenge: design an agent with oversight