Uplift7

> Still reversing a linked list on a whiteboard?

Hire engineers who can steer the agents.

Uplift7 is an AI-native assessment platform for engineering teams. Candidates solve real problems by directing AI agents in a live sandbox, and you see exactly how they frame, steer, and verify the work.

Request accessHow it works
session.log
  1. 00:02candidateRead the failing test and explain the root cause before changing anything.
  2. 00:04agentProposes replacing the token bucket with a fixed window.
  3. 00:05candidateNo. Keep the bucket; the bug is refill timing. Write a boundary test first.
  4. 00:09agentTest added. Patch applied. All tests passing.
  5. 00:11candidateRun the load script and check latency before we call it done.
  6. 00:13agentLoad test at 2k req/s: p50 4ms, p99 31ms. Two 429s logged at the window boundary.
  7. 00:14candidateThose 429s are expected under burst. Document the limit in the README, then open the PR.
  8. 00:17agentPR #142 opened: refill timing fix, boundary test, README note. CI green.
  9. 00:18candidateSelf-review the diff once more and flag anything you're unsure about.
Problem framing: 4 of 5
Agent steering: 5 of 5
Verification: 4 of 5
Outcome: 5 of 5
Built for
  • Recruiting platforms
  • Hiring managers
  • Recruiters
  • Engineers

// The shift

Writing code is no longer the hard part.

AI agents now write much of the code. What separates strong engineers is how well they frame a problem, direct an agent, catch its mistakes, and get to a result that actually works. Whiteboard puzzles and timed coding tests don't measure that. Uplift7 does.

// How it works

From real scenario to structured signal.

  1. Step 1: Configure

    Choose a real-world scenario.

    Pick from a library of engineering tasks or bring your own. Set the role level, the agents and models available, and the time box.

  2. Step 2: Assess

    Candidates work with real agents.

    Each candidate gets an isolated sandbox with production-grade AI agents. They plan, prompt, review, and correct, the same way they would on the job.

  3. Step 3: Observe

    Every decision is captured.

    Uplift7 records the whole session: each instruction, each agent step, each correction, each test run. You judge the process, not just the final diff.

  4. Step 4: Report

    Get a comparable, evidence-backed score.

    Reviewers receive a structured report on problem framing, agent steering, verification, and outcome, with the session replay behind every score.

// Why Uplift7

Built for teams hiring AI-native engineers.

  • Signal that matches the job

    Measure the skill engineers use every day now: directing AI agents toward working software. Not syntax recall, not trick puzzles.

  • Proof that travels

    Share the report with a hiring panel or a client. They see the work itself, not your notes from a call, and anyone who questions a score can watch what happened.

  • Fair, consistent, hard to game

    Every candidate gets the same sandbox, agents. Using AI isn't cheating here; it's the point. Reviewers compare like with like.

// Early access

Assess engineers the way they work now.

The best AI-native engineers are hard to spot. We're helping a small group of ambitious teams find them first. Tell us where you hire and we'll be in touch.

Write to us at hello@uplift7.com

// About

Built by people who build agent infrastructure.

Uplift7 exists because the way software gets built has changed faster than the way engineers get hired. We spend our days running AI agents against real codebases, and we kept seeing the same thing: the engineers who get the most out of agents aren't always the ones who ace a coding test.

So we built Uplift7 around that skill: an AI-native assessment for engineering teams, not a general coding tool repurposed for interviews. Every session runs in an isolated sandbox, with the agents and models your team actually uses.

Uplift7 runs on agent and sandbox infrastructure developed by the team at Multi.