Free workflow walkthrough
An AI-assisted bug triage workflow that does not invent a diagnosis
AI is useful in bug work when it helps structure evidence, compare hypotheses, and keep the fix narrow. It becomes risky when a plausible story is presented as a root cause before the failure is reproduced or the relevant code path is inspected.

01 / Scenario
Start with the boundary, not the model.
Imagine that patients can double-book a physical slot near the spring daylight-saving transition, but only on reschedule in Europe/Bucharest. A useful triage process preserves that narrow scope until evidence proves otherwise. It does not rewrite scheduling rules for every timezone because the symptom sounds temporal.
An AI assistant may organize supplied facts, suggest questions, and draft a bounded artifact. It cannot turn an unverified claim into repository evidence, stakeholder approval, a test result, or production truth.
02 / Workflow
A repeatable sequence.
- 01
Make the report actionable
Capture build, environment, user path, expected behavior, actual behavior, frequency, impact, and existing evidence. Separate what the reporter observed from what the team assumes.
- 02
Reproduce—or bound non-reproduction
Record exact setup, inputs, timestamps, versions, and outputs. If it does not reproduce, return the matrix you tried and the missing signal needed next instead of declaring the issue invalid.
- 03
Compare the failing path with a control
A nearby working path often narrows the search faster than reading the whole system. In the timezone example, new booking already normalizes through the scheduling authority while reschedule does not.
- 04
State the smallest supported cause
Name the condition, the mechanism, and the evidence that connects them. List plausible alternatives that were ruled out and those not yet tested.
- 05
Design the minimal repair
Correct the cause at the existing authority boundary. Preserve unrelated policy and avoid opportunistic cleanup unless it is required for safety.
- 06
Protect the invariant
Write a regression that fails for the original defect, control tests for adjacent behavior, and rollout/rollback checks appropriate to the risk.
03 / Fictional worked excerpt
What a bounded output looks like.
The table below condenses a fictional example included for demonstration. It is not a customer result, production test, approval, or performance claim.
Reschedule compares a naive local value with stored UTC instants
New booking already uses timezone-aware normalization
Route reschedule through the existing normalization function
Other timezones and historical overlaps require separate verification
04 / Failure modes
Where otherwise plausible AI output goes wrong.
- Diagnosing from the ticket title
- Changing global restrictions to make one reproduction pass
- Writing a test that mirrors the implementation instead of the invariant
- Claiming production repair from a local or fictional example
05 / Read-only checklist
Use this before calling the artifact complete.
- Exact environment and build
- Expected and actual behavior
- Reproduction log or bounded non-reproduction
- Working control path
- Cause, mechanism, and supporting evidence
- Minimal repair boundary
- Regression plus control cases
- Rollout signal and recovery path
This checklist is intentionally read-only: it helps you inspect a workflow without exposing the full native skill files, portable prompts, templates, or the bundle-exclusive orchestration skill.
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What should AI do during bug triage?
It can structure the report, propose a reproduction matrix, compare evidence, generate bounded hypotheses, and help draft regression cases. It should not claim a root cause or test result it has not observed.
What is a minimal fix?
A repair that corrects the evidence-supported cause with the smallest reasonable regression surface. Minimal does not mean skipping tests, rollout controls, or required safety work.