DeftBench
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For teams hiring engineers who build with AI

Stop interviewing for a job AI already changed.

DeftBench is an AI-native coding assessment. Candidates direct a real AI agent through an ambiguous, real-world problem inside a live browser IDE, and you get an evidence-linked evaluation of how they actually worked, in minutes.

Run your first assessment freeSee a sample report
workspace provisioning
<60s
session to report
<10min
evidence-linked scores
100%

Latency figures are engineering targets from the product spec.

Sample evaluation
Candidate #4471

4.3/5

Strong hire
Candidate #4483

3.1/5

No hire
  • Chose a sliding-window limiter over the agent's fixed-bucket suggestion, and could explain why.
  • Rejected a suggestion that skipped input validation on the retry path, with a stated reason.
  • 62 tool calls, 9 accepted diffs — every line here opens the replay at that moment.
  • Real browser IDE with an AI agent, wired in
  • Signup to first assessment in one sitting
  • SSO, audit logs, and BYOK
The problem

Your interview process is testing for a version of the job that's already gone.

  • unmeasured

    Candidates are told “no AI,” then use it anyway: off camera, unmeasured, ungraded.

  • shallow

    The ones who are allowed to use AI get a chat sidebar bolted onto a LeetCode clone. You get a pass/fail, and nothing about how they got there.

  • misaligned

    Meanwhile the actual job is to direct an agent, verify its output, catch what it gets wrong, and know when to override it. None of that shows up in a whiteboard algorithm question.

You're left guessing. Six months later you find out whether the hire could actually work this way, after the offer, after the ramp, after the org chart already moved.

A hiring signal you can trust, not a vibe you have to defend in the debrief.

Imagine handing your hiring manager a report that says, with a timestamp and a replay link: this is exactly the moment the candidate caught the agent's bug, here's the prompt that fixed it, here's where they didn't verify and it bit them.

Evaluation report — candidate #4471Strong hire signal
  • Caught an unsafe default the agent introduced, and verified the fix with a test before merging.14:02
  • Redirected the agent after a vague first prompt produced the wrong abstraction.03:41
  • Shipped without running the test suite once. Flagged for review.21:15

That's a decision, not a transcript dump.

How it works

Five steps, one evidence trail.

  1. 01

    Give candidates a real workspace

    A full browser-based IDE with an AI coding agent already wired in, the same class of tool they'll use on day one. No install, under 60 seconds to spin up.

  2. 02

    Give them an ambiguous problem

    Open-ended engineering work of the kind that rewards judgment, research, and tool selection rather than memorized algorithms.

  3. 03

    Capture everything that matters

    Every prompt, tool call, MCP invocation, and accepted or rejected suggestion, captured as structured telemetry rather than a video you have to scrub through.

    {
  "event": "suggestion_rejected",
  "tool": "edit_file",
  "reason_inferred": "missing_input_validation",
  "t": "00:14:02"
}
  4. 04

    Get an evidence-linked evaluation

    Every score traces back to a specific, replayable moment in the session, so a debrief can open the moment instead of arguing about it.

  5. 05

    Decide faster, with confidence

    A recruiter dashboard built for comparing candidates at a glance, instead of re-reading raw transcripts one at a time.

Why this, not that

The comparison that matters.

How DeftBench compares with traditional and bolt-on AI coding tests
CapabilityTraditional coding testsBolt-on "AI-allowed" testsDeftBench
Tests the actual job
Measures how AI was used
Evidence-linked scoring
Setup timeHoursHours< 5 min
Report you can defend in a debrief
What you get

Every score, traceable to the moment it came from.

Evidence, not a rating
Every score links back to a timestamped moment in the session you can open and read for yourself.
The whole session, structured
Prompts, tool calls, MCP invocations, and each accepted or rejected diff are captured as queryable telemetry.
Isolated by construction
Every session runs in its own container, provisioned fresh for the candidate and torn down afterwards.
Replay over transcripts
Step back through how the work actually unfolded instead of re-reading a wall of chat logs.

We'll publish measured latency numbers, and named customer references, once pilots close.

Who it's for

Built for every stage of hiring.

AI-native startups
Fast setup, no process overhead, hire for the tools you already use.
Scaleups
Standardized rubrics and dashboards, so every interviewer's signal means the same thing.
Enterprises
SSO, audit logs, data residency, BYOK.
Recruiting agencies
Multi-tenant client workspaces, white-labeled reports.
Frequently asked

Questions, answered.

No — it's measuring the thing you actually care about: whether they can direct, verify, and override an agent. Unsupervised AI use in a "no AI" interview is the actual blind spot.
The onboarding flow is built to get you from signup to your first assessment sent in a single sitting — no install, no infrastructure work, no scheduling call.
That's fine — assessments are configurable per role, and the report shows you exactly where a candidate's process holds up and where it doesn't, regardless of your current process.

Give your next candidate a problem worth solving.

We're onboarding teams in small batches. Book a 20-minute call and we'll walk you through a pilot.

Book a meetingContact us