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Example scenario

Screening 200 applicants for an AI-native backend role

A small team needs a shortlist from a large applicant pool without spending engineer hours on every first round. A general assessment link and the candidate board do the first pass.

This is an illustrative scenario, not a customer story. The team described is hypothetical; the setup, features and report signals are what PraxisAI provides today.

The problem

A two-person engineering team is hiring its third backend engineer. The posting draws a couple of hundred applicants, most of whom list AI coding tools on their resume.

A take-home for everyone is unreviewable at that volume, and a live first round for everyone costs more engineer time than the team has. Resumes alone cannot separate people who use an agent well from people who paste whatever it says.

The approach

  1. 01

    One link in the job post

    The team creates a general link from the portal, sets max uses to the size of the pool and a two-week expiration, and pastes it into the application confirmation email. Candidates sign in with Google, see what is recorded, and start when they are ready.

  2. 02

    Rotating tasks from a difficulty band

    Instead of one fixed issue that could leak, the link draws from the medium band of the SWE-bench bank, so candidates get comparable but different tasks from real repositories such as Django, Flask or SymPy.

  3. 03

    Sort the board, read the top of it

    Every round lands on the candidate board with status, score, solved or not, tokens used, time taken and any integrity flags. The team sorts by score, opens the full reports for the top group, and compares finalists side by side.

What the report shows

The signals a team would read in this scenario, all recorded in every round. See them in the sample report.

Outcome
Did the hidden failing tests pass after the change, without breaking the tests that already passed?
Verification
Did the candidate run tests themselves before submitting, or trust the agent's word?
Prompt quality
Were prompts specific, with context and constraints, or vague one-liners repeated until something worked?
Token economy
How much agent usage the fix took, so two equal outcomes can still be told apart.
Integrity flags
Long focus losses, large pastes and pastes after leaving the tab are flagged for a human to look at, not auto-rejected.

The takeaway

The first round becomes something the team reads rather than runs. Engineer time goes into the final interviews, and those interviews can start from what the report shows about each finalist.

More scenarios

See it on your own hiring loop.

Tell us about the roles you hire for and the assessment format you want to improve.