AI Minority Lab · Hiring teams
Looking for a practical way to evaluate candidates forAI Engineers, Forward Deployed Engineers, Solutions Engineers, AI Consultants
Give candidates a realistic enterprise AI deployment problem, not another abstract take-home exercise.
Free of charge for candidates during private preview.
The challenge provides evidence for a structured hiring conversation. It does not replace interviews, references, or your own hiring judgment.
The hiring gap
A polished demo hides the decisions that matter in production.
Candidates can explain an agent architecture or make a happy-path demo work. That still leaves the hard questions unanswered.
What happens when the workflow has authority to move money? When policy is ambiguous? When an API times out after committing a change? When escalation is the correct outcome?
AI Minority Lab creates a shared operational problem you can discuss through concrete decisions and consequences.
Free of charge · Private preview
Safe Refund Agent
A candidate builds an AI workflow for an overloaded e-commerce support operation. It must investigate evidence, apply policy, take permitted actions, and avoid duplicate or unauthorized refunds.
Controlled failure · HTTP 504
The refund API timed out. Did the request fail, or did the workflow pay twice?
The candidate must inspect the resulting state and recover safely. Their choices create a more useful technical conversation than a framework-specific coding prompt.
Open the free challenge →18 / 20cases completed correctly
Diagnostics
- 0 duplicate refunds
- 1 missed escalation
- Incorrect refund amount: USD 1,250.00
Sample metrics for orientation only. Not a live learner result.
What the work sample surfaces
Evidence of deployment thinking, not prompt memorization.
- 01
Integration judgment
Can the candidate discover a stateful API, follow relationships, and build a working local integration?
- 02
Operational control
Do they respect permissions, policy, financial limits, and the boundary between automation and escalation?
- 03
Failure handling
Can their workflow recover from ambiguous timeouts without duplicating a consequential action?
- 04
Outcome evidence
Can they explain what the workflow changed, where it failed, and how they would improve it?
How it works
Build. Deploy. Prove.
- 01
Build
The candidate creates a workflow locally using any model, framework, language, or architecture.
- 02
Deploy
They connect it to a simulated company and handle tickets, orders, policy, permissions, and side effects.
- 03
Prove
Deterministic checks inspect the resulting enterprise state and produce an objective deployment result.
Candidate-owned evidence
The candidate controls their account and work. They choose what to share with a hiring team; employers do not receive private access to learner activity or results.
Use a more concrete starting point
Let the candidate show how they handle consequential AI work.
The Safe Refund Agent challenge is free of charge during private preview. Hiring pilots and custom evaluation work are separate, case-by-case conversations.