Reading and Debugging Agent Traces
Read an agent's step by step record and find where and why a run went wrong
A taste of a lesson
My agent called search_orders five times with the same arguments and then gave up. What should I check?
Look at what search_orders returned the first time. Repeated identical calls usually mean the result did not tell the model anything it could act on: an empty list, a vague 'error', or a timeout with no explanation. If it returned an empty list, the model may suspect a glitch and retry. Change the tool to say 'no orders found for this email; check spelling or ask the customer for an order number'. Then rerun the same request a few times. What exactly did the first call return in your trace?
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
- List what a trace must record to make agent failures diagnosable
- Locate the first wrong step in a run and inspect what the model saw there
- Classify failures by cause: context, tool design, tool result, instructions or model
- Recognise loops, premature completion and invented tool results
- Turn every fixed failure into a regression test
Lesson plan
- 1 Anatomy of a useful trace Know what a trace must contain to support real debugging. Start
- 2 A reading method Read a trace in a fixed order that leads quickly to the problem. Start
- 3 Classifying causes Assign each failure to a cause category that suggests a fix. Start
- 4 Recognising failure patterns Spot common patterns at a glance and know what each usually means. Start
- 5 Fixing and proving the fix Change one thing, confirm it works across runs and prevent regressions. Start
- 6 Traces, privacy and retention Handle traces responsibly since they often contain sensitive data. Start
Try asking
About this tutor
For developers who have watched an agent fail and could not tell why. A trace is the full record of a run: prompts, model decisions, tool calls, arguments, results, errors and timings. In this tutor you learn what a useful trace contains, how to read one quickly, and a method for locating the first wrong step and its cause: missing context, a misleading tool description, a bad tool result, a vague instruction or a model limitation. You practise on realistic broken traces and finish each with a fix and a test that would catch the problem again.
Reviews
4.3
4 ratingsSample
- Filip S.Sample
Solid, but I already had decent tracing, so the first two lessons were review for me. The patterns lesson was the useful part.
- George K.Sample
Good practice traces. I would have liked one with subagents in the mix, but the method transfers.
- Ines B.Sample
'Find the first wrong step, then ask what the model saw' fixed my habit of blaming the model. Most of our failures turned out to be tool results.
- Amara U.Sample
We started versioning prompts after lesson one. Debugging is so much faster when traces match the code.
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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