The interview no longer measures the job.
You hired the person who shipped the take-home. Three weeks in, they're lost the moment the agent stops driving.
It isn't their fault. The take-home told you they finished. It didn't tell you they could have made the call.
Eighty-five percent of developers use AI coding tools daily. The engineer you hire on Monday opens Claude Code before they open Slack. Their job is to orchestrate a system — model, tools, their own judgment — toward working software. Your interview asks them to mentally simulate a compiler in a sandbox that doesn't exist.
The interview hasn't measured the job for a while. It's just gotten more elaborate about pretending it does.
Why the patches aren't working
Every assessment vendor in this space has shipped “AI features” in the past 24 months. AI assistants inside the sandbox. AI-generated rubrics. AI proctoring overlays. None of it matters, because they're all still grading the output.
They built environments that look like the job and aren't. Bolting AI onto a fake environment doesn't make it real — it just makes the theater more elaborate.
The other move — banning AI from the interview entirely — is worse. You aren't measuring the wrong thing; you're filtering for a skill that actively predicts poor performance in 2026. The engineers who'll ship for you are the ones who've already integrated AI deep into their workflow. Your loop is filtering them out and calling it rigor.
Both approaches share one root error: they treat AI as the problem. It isn't. It's the context. You have to measure inside it.
Correctness is cheap now. Process is expensive.
What you actually get
Your candidate runs one command and works in Claude Code — the tool they'd use on day one. We capture every prompt, every tool call, every file edit, with attribution down to which lines they wrote and which the model wrote.
That feed becomes three things your hiring loop has never had:
A replay you can scrub. Watch the session like a Loom: the decision points, the pivots, the moments they caught the model being wrong, the moments they didn't. Your hiring manager spends fifteen minutes and knows more than a 45-minute on-site would have told them.
A hiring brief, not a score. One paragraph: where this candidate showed judgment, where they didn't, what to probe in the on-site. The thing your EM was going to write from gut feel — written from the actual evidence instead.
An orchestration score that means something. Calibration in progress against a senior-engineer cohort, weighted on the signals that predict performance in an AI-native workflow. Not “did they finish.” How they got there.
And — because it's the first question every buyer asks — candidates see a consent screen before anything is captured. Every event type we record, every one we explicitly don't. Redaction is built in. This isn't surveillance. It's a structured window into real work, scoped to the assessment, with the candidate's eyes open.
What changes for you
Three things.
Your reviewer time collapses. In beta, OA review time dropped 80%. The replay surfaces the decision points; the brief writes the on-site probe questions. Your EM stops watching candidates type and starts reading the parts that matter.
You see orchestration ability before the offer, not week three. The candidates who looked great on paper and went quiet by week three — that signal lives in the workflow. Now you can see it before you commit.
Every decision has receipts. When the loop disagrees about a candidate, you scrub to the moment in question. No more “I just got a feeling.” The replay is the appeal.
How to get on it
We're picking twelve founding teams personally. Weekly 45-minute call with the founder, on the record, walking through your actual replays. Everything unlocked. Founding price locked through 2028 — if we raise, you don't.
If you hire senior engineers and you've sat through one too many take-home reviews that didn't tell you anything new, book a call. We'll talk through your roles. If it isn't a fit, we'll say so on the call.