Review an AI-assisted debugging assessment

Agree the AI policy before the attempt

Decide whether AI use fits the work you are assessing, and state the policy in candidate instructions. Use the same policy for comparable attempts on a frozen assessment release. Do not change your interpretation of permitted tool use after seeing a result.

When the Buglyst assistant is enabled, its requests are recorded as context. Candidates can also disclose outside tools and how they verified the output. Outside use is self-reported: Buglyst cannot see other tabs, applications or conversations.

When AI is not allowed, the Buglyst assistant is disabled for the attempt. That is an enforced workspace setting, not proof that no outside tool was used. Do not promote the assessment as “AI-proof” or infer misconduct from a short patch or a fast completion.

Separate recorded work from unknowns

First confirm the report belongs to the intended assessment version and conditions. Then open the exercise evidence and recorded steps. Read candidate explanations in their own words before attaching your interpretation.

Evidence completeness describes the available work trail, not a statistical probability of job performance. Missing notes or telemetry limit what you can infer. Similar submitted fixes and exercise exposure are reviewer context; they do not establish copying.

Read the verification state accurately

Visible checks passed means that recorded visible run passed. It does not establish that all required private checks passed or that a later edited patch was verified.

Private checks passed supports the final verdict for the submitted patch bound to those checks. Private checks and reference solutions remain hidden from candidates and reviewers. Passing is evidence of required behavior under the assessment contract; it is not a hiring recommendation.

Private checks failed means the required verification did not accept that submission. Discuss the gap using recorded results and the stated behavior. Do not invent the hidden test source or treat a failure as a character judgment.

Unverified or pending is an incomplete result, including unavailable execution. An infrastructure error is not a failing candidate solution. Avoid treating a missing verdict as a pass or using it in a completed-result comparison.

Discuss the submitted work with the candidate

Use a short technical conversation around the actual submission. Give the candidate the chance to explain context you could not observe. Keep the discussion tied to the job responsibility and the same questions you would ask other candidates.

1. Cause: “Show where the symptom originates. What observation ruled out your first alternative?”
2. Change: “Why does this patch address that cause? What behavior did you deliberately preserve?”
3. Verification: “Which recorded check supports the change? What important boundary would you test next?”
4. AI suggestion: “If you used an assistant, what did you accept, reject or change, and how did you check it?”
5. Uncertainty: “What remains unverified, and what would you do before releasing this fix at work?”

Ask about the reasoning behind an assistant suggestion rather than the number of assistant requests. A candidate can use AI and still demonstrate careful verification; another can avoid AI and leave the same important behavior unchecked.

Record your judgment separately

In a completed candidate report, the reviewer debrief records the hiring stage, whether the report informed your next step and a concise evidence note. Use the note to connect your judgment to a recorded observation and the candidate’s explanation. Feedback remains separate from measured work and is not a predictive score.

Use compatible releases and conditions when comparing attempts. For company-project pilots, an internal baseline is labelled descriptive context, with sample size shown and at least three eligible compatible internal completions required for aggregates. It is not an industry benchmark, and faster than the baseline does not imply a better engineer.

Buglyst does not automatically reject candidates, predict hiring success or establish employee quality. Your team decides how this work sample fits with interviews, experience and other relevant evidence.

Start with the clearly labelled illustrative report. If you are setting up the task, follow the backend assessment design guide. Uploaded company projects remain a controlled pilot pending production runner verification; these review principles also apply to the available standard library assessments.