Hire engineers who are force-multipliers with AI — not people who rubber-stamp it
Everyone uses AI now. The question that actually predicts performance is whether a candidate commands it well. Copilot Command scores how they orchestrate an AI pair engineer on a real task — and shows you the replay.
Four sub-scores
We measure orchestration, not just output
A candidate can ship working code by accepting whatever the model says. Copilot Command separates the operators from the rubber-stampers across four dimensions.
Delegation quality
Does the candidate hand the AI the right work with clear, well-scoped instructions — or dump vague prompts and hope?
Verification discipline
Do they read, test, and challenge what the model produces, or rubber-stamp generated code into the branch?
Failure recovery
When the AI proposes something flawed, do they catch it, diagnose it, and steer to a correct fix?
Judgment
Do they know when to trust the AI, when to override it, and when to stop and think for themselves?
Defensible by design
Every orchestration score comes with its replay
Reviewers see exactly how the candidate worked with the AI: what they delegated, how they verified, where they caught a flawed suggestion, and how they recovered. When a decision is questioned, the evidence is right there — not a black-box number.
- Deterministic scenarios with scripted flaws, so every candidate faces the same test.
- Reproducibility pins and session replay make each score auditable end to end.
Illustrative score report.
See how a real candidate orchestrates AI
Walk through the reviewer replay hiring teams use to defend every orchestration score.