Teaching debugging through production incidents

A traditional coding assignment tells you what is wrong. A real production incident does not. I ideated, designed and built Debug Simulator at Scaler so learners could investigate a broken system, explain its root cause, and see whether their fix actually worked. To mimic the actual job that a developer would do in real life.

Scaler trains working professionals and school graduates for software and business careers. Learners pay for a job outcome, so course time needs to build skills they can use at work. Instructors and content teams supply the subject briefs; the authoring pipeline turns them into playable cases.

The simulator is used as part of the curriculum, with cases lasting about 23 minutes on average. 95% of positive reviews from learners.

Traditional coding assignments hand over the bug and ask only for a fix, so the investigation never happens. Learners could write the fix but had not practised finding the fault, reading partial evidence in an unfamiliar system, or explaining why the failure happened now.

A normal assignment gives the input, expected output and function to write. A real incident might only say that checkout has been slow since this morning. The developer has to understand the system, read logs and metrics, connect clues, explain why the failure happened now, and communicate a root-cause analysis.

Harder assignments would have been cheaper to build, but kept the same limitation. Learners could ask an AI to solve a stated problem and skip the investigation. The format itself had to change.

The investigation asks for more than naming a broken component. Learners explain the causal chain in their own words and separate an immediate mitigation from a lasting fix. Feedback then makes the quality of that reasoning visible.

A chosen fix changes the simulated system. Treat a symptom and the indicators dip, then return. Fix the cause and they settle. Learners see the consequence of their reasoning.

An end score alone cannot show how a system behaves after an intervention. Applying the action closes that gap. The review explains what was incomplete and how the learner can approach the next incident differently.

Instructors provide a structured Markdown brief. An agent pipeline I built on Claude Managed Agents, on Anthropic’s API platform, authors the case, reviews teaching and functionality, generates narration and art, and opens a pull request. I test on staging before releasing.

Writing 50 cases by hand would have made content production the bottleneck. I designed a Markdown template that instructors complete with Claude, which asks questions until the context is sufficient. Uploading the brief through the admin interface starts the pipeline on Claude Managed Agents.

• The author builds the case using reusable modules for logs, graphs, questions and system nodes.
• The content reviewer checks correctness and readability.
• ElevenLabs and an in-house voice generate the coach’s narration.
• A functional reviewer plays the case in a browser and checks interactions, calculations, logs and facts. A final reviewer repeats the browser review after edits.
• GPT Image 2 generates the case poster within a controlled visual style.
• The GitHub step checks conflicts and opens a pull request. A Slack notification brings me back into the release process.

I kept a human as the final release gate. A clean pull request still goes through staging, testing and a deliberate production release.

When a learner reports a bug or a confusing part of the experience, I contact them, run a short interview, fix the issue, deploy and tell them what changed. The reported resolution turnaround is three hours. Keeping this loop manual helps connect production decisions to a learner’s actual experience.