Monitoring Agents in Production
Know what your live agent is doing, catch quality drops early and respond to incidents with a plan
A taste of a lesson
Our support agent's thumbs up rate looks steady, but complaints to managers went up. How can both be true?
Easily. Thumbs ratings come from a small, self selected group, and a confident wrong answer can still get a thumbs up in the moment. The complaints are the stronger signal. Pull the conversations behind recent complaints and a random sample of ordinary runs, and review them by hand, labelling what went wrong. Also check whether anything changed recently: prompts, tools, the model, or a new product launch bringing new questions. Exercise: list three changes in the last month that could affect your agent's answers.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Decide what to log per run while protecting personal data
- Choose signals that reveal quality, cost and safety problems
- Run weekly sampled human reviews and feed findings into evaluations
- Set alerts tied to actions and owners instead of noise
- Follow an incident checklist with containment, fallback and review
Lesson plan
- 1 What to record Log enough about each run to diagnose problems without hoarding sensitive data. Start
- 2 Signals that matter Select a small set of signals that each lead to a decision. Start
- 3 Sampled human review Catch failures metrics miss by reviewing runs every week. Start
- 4 Alerts without noise Design alerts that fire on real changes and reach someone who will act. Start
- 5 Incidents and fallbacks Respond to agent misbehaviour calmly with containment first. Start
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About this tutor
For engineers and operations leads running agents with real users. Once an agent is live, problems arrive from new kinds of requests, changes in tools or data, model updates and misuse. This tutor covers what to log, which signals to watch (success rates, escalations, tool errors, cost per task, unusual actions), how to sample runs for human review, how to collect user feedback that means something, how to set alerts that are not noise, and how to run a calm incident response when the agent misbehaves. You will finish with a monitoring plan and an incident checklist for your own agent.
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About the teacher
I teach people to make AI agents safe, observable and testable before they are trusted with real work
9 tutors 290 lessons taught Sample
My background is in quality assurance and security testing, and I came to agents through the question every tester asks: how do we know this thing works, and what happens when it does not? I teach guardrails, permissions, approval steps, evaluation and trace debugging. My lessons are concrete. We look at a failure, name it, and design the check that...
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