See whether senior engineers can safely own production systems in the AI era.
Put candidates inside a controlled production incident, let them investigate with AI available, and see how they reason, manage risk and verify recovery - not just whether they eventually find the bug.
Human hiring decision. Evidence, not an automated verdict.
AI is breaking the signal used to hire senior engineers.
What hiring usually observes
- -Code produced
- -Tasks completed
- -Interview answers
- -Speed and fluency
AI can amplify all four.
What senior engineers are actually trusted to do
- +Investigate incomplete evidence
- +Make decisions under uncertainty
- +Recognise operational risk
- +Challenge AI when it is wrong
- +Verify that the system is genuinely safe
The problem is no longer only “can they produce the answer?” It is “can we trust them to own the outcome?”
The strongest senior-engineering signal appears when the answer is not obvious.
Investigate
Find the evidence that matters.
Reason
Form and revise hypotheses.
Decide
Choose proportionate, reversible actions.
Supervise AI
Know when to rely on it and when to challenge it.
Verify
Prove recovery rather than assume it.
Senior engineering is increasingly about making good decisions under uncertainty. Hiring rarely creates that uncertainty deliberately enough to observe it.
We create the missing signal.
A live production-ownership assessment for senior backend engineers.
Enter a controlled production incident
A running service is failing. Evidence is incomplete and several explanations are plausible.
Investigate and act
The candidate inspects logs, traces, code and system state, forms hypotheses, takes actions and uses AI as part of the workflow.
Show how they own the recovery
We capture investigation, decision-making, operational risk, AI supervision and verification - not simply whether the bug was eventually fixed.
We do not ask whether the candidate and AI can fix the incident. We ask whether the engineer understood, controlled and safely verified the recovery.
A payment provider times out. The system cannot tell whether the charge succeeded.
The candidate must investigate state, evaluate the AI suggestion and decide how to act without creating new harm.
The incident makes judgment observable.
Does the candidate inspect state before acting?
Do they distinguish symptom from cause?
Do they revise their hypothesis when evidence changes?
Do they recognise the consequence of being wrong?
Do they challenge AI when its recommendation ignores important state?
Do they choose reversible actions under uncertainty?
Do they verify customer recovery, not just service recovery?
Do they communicate what remains unresolved?
The candidate is not being tested on whether they know a trick. They are being tested on whether they can safely own the consequences of a decision.
The output is evidence, not a score.
AI changed the work - so hiring has to change too.
Before AI
With AI
The scarce skill is no longer just writing software. It is staying accountable when software is produced with AI.
Built for a different question.
Coding assessments
Can they complete a technical task?
AI-native work trials
Can they build or debug software effectively with AI?
Production Ownership Assessment
Can they safely own an AI-assisted production failure?
Others test delivery. We test accountability under failure.
Our initial focus is high-consequence senior backend hiring, using causal incident simulations where candidate decisions change system and customer state.
Start where the cost of getting it wrong is obvious.
Primary customer
Engineering organisations hiring senior backend engineers into high-consequence systems.
Initial segment
Buyer
Core use case
Replace one senior-backend interview round with a Production Ownership Assessment.
Initial role
Senior Backend EngineerWe are starting here because the consequence of a wrong hire is clearest. The approach extends to other high-consequence engineering roles over time.
A realistic challenge, not a surveillance test.
AI is allowed.
Candidates work with AI as they would in the real job. The assessment is about how they supervise it, not whether they use it.
Transparent dimensions.
The candidate is told what is being assessed: investigation, reasoning, decision-making, AI supervision and verification.
Judgment, not syntax.
The environment tests professional judgment under realistic uncertainty, not memorised commands or framework knowledge.
Evidence, not initial guesses.
One wrong initial hypothesis is not failure. How the candidate responds to evidence is what matters.
Human decision.
A human hiring team makes the final call. There is no automated hire or no-hire verdict.
No proctoring.
No webcam, keystroke monitoring, audio recording or behavioural surveillance. The assessment captures actions, not biometrics.
Production incidents are the first checkride.
We are starting with senior backend hiring, but the deeper need is broader: proving engineers can safely operate AI-assisted systems.
AI can increasingly do the work. Companies still need to know who they can trust to own it.
Help define the new standard for senior engineering assessment.
We are working with a small number of engineering teams to run Production Ownership Assessments in real hiring processes and shape the first version of the product.
Human hiring decision. Evidence, not an automated verdict.