Altor evaluates engineering candidates on Cursor, Claude Code, and GitHub Copilot proficiency — so your team doesn't have to build this from scratch.
Your engineers use Claude Code and Cursor every day. Your technical interviews still ban them. That gap is costing you good hires and letting bad ones through.
Closed-book algorithm memorization has no correlation with how well an engineer uses AI agents on the job. You're optimizing for the wrong signal.
You can't tell if the candidate wrote the code or pasted the ticket into Claude Code at midnight. Take-home signal collapsed in 2025.
Designing an AI-agent interview format, rubric, and scoring system from scratch takes weeks. Your eng team doesn't have it. We already built it.
Karat, CoderPad, HackerRank — none of them evaluate AI agent fluency. They test traditional coding. The gap in the market is total.
We run a 90-minute live AI agent proficiency assessment on your behalf. You get a scored report and a clear hire/no-hire recommendation.
We learn your stack, the role, seniority level, and what "good" looks like on your team. We customize the interview format and the rubric to match your actual engineering workflow — not a generic template.
We handle scheduling directly with your candidate. The interview is live, 90 minutes, screen-shared, real repo. The candidate uses their own AI tools — Cursor, Claude Code, Copilot, whichever they prefer. We conduct the session and observe in real time.
After the live session, we review the candidate's AI session transcript — their prompts, tool calls, rejections, and iteration patterns. This is where the real signal is. We score against the rubric across four dimensions.
You receive a structured report: dimension-by-dimension scores with evidence, key excerpts from the session transcript, observed red flags and green flags, and a clear hire/no-hire recommendation with reasoning.
Our rubric was built from practitioner experience, adapted from AI-enabled interview formats at Meta, Google, Canva, and Sierra. It scores what actually predicts performance — not what's easy to test.
Does the candidate catch model errors? Write tests? Question confidently wrong output? This is the safety reflex that keeps AI-native teams from shipping hallucinated code.
How many turns to a usable result? Do prompts include scope, constraints, and codebase context? Token efficiency as a hiring signal.
Can they explain every line in the diff? Can they defend decisions under questioning without the AI present? Ownership is the final gate.
Can they decompose tasks correctly? Fan-out vs. sequential decisions? Do they use multi-agent patterns appropriately for the complexity of the problem?
| Capability | Karat / CoderPad / HackerRank | Altor AI Agent Interview |
|---|---|---|
| Evaluates AI tool proficiency (Cursor, Claude Code, Copilot) | ✗ Not offered | ✓ Core focus |
| Token efficiency scoring | ✗ Not offered | ✓ Included in every report |
| Session transcript review | ✗ Not offered | ✓ Analyzed and annotated |
| Rubric calibrated to your team's AI workflow | ✗ Generic template | ✓ Customized per role |
| Live interview with AI tools explicitly allowed | ✗ Typically banned or ignored | ✓ Required — we score how they're used |
| Traditional algorithm / syntax evaluation | ✓ Primary offering | Optional add-on |
| Volume commitments required | ✗ Yes (Karat minimum commitment) | ✓ No — pay per interview |
| Pricing transparency | ✗ No public pricing | ✓ Contact for quote, no commitment required |
Karat's standard interviews run $200–$450 per session and do not include any AI agent evaluation. See full comparison →
We price per interview. No annual commitments, no volume minimums. Start with one interview and scale from there.
Book a 30-minute discovery call. We'll learn your role, your stack, and what "great" looks like on your team — then run the first interview within a week.
Related: Complete guide to AI agent interviewing · Download the scoring rubric · Altor vs. Karat for AI agent interviews