What do you actually assess?

Realistic AI-native work samples: did the candidate understand the task, direct AI agents well, verify the work, recover from bad output, and ship the requested deliverable?

How do you handle candidates using AI?

Agentic is the default assessment condition. Every candidate gets the same bounded model/tool access, so the signal is workflow skill rather than wallet.

What evidence does a recruiter get?

A 0-100 score, five subscores, recommendation, confidence, strengths, risks, next-interview questions, and replayable session evidence.

Do you still support no-AI assessments?

Yes. Classic No-AI stays available as a baseline/control condition for compliance, calibration, or teams that cannot allow AI yet.

What about integrity?

Timed sessions with grace windows plus process signals, so the evidence reflects real work rather than a single final paste.

{A}

Read app.js L1-84

proof

const form = query('#contact')

function validate(fields) {

return errors.length === 0

Web searched inline validation UX

proof

labels stay visible

errors near inputs

keyboard path preserved

Changed 3 files +59 -9

proof

+ setError(field, message)

- form.submit()

+ if (!validate()) return

Validated browser clicks passed

proof

empty submit -> errors

bad email -> inline note

success copy visible

plan

edit

run

click

report

// Assessments

Can this candidate ship real software with AI agents?

AlgoArena measures AI-native engineering: planning, agent direction, delegation, verification, recovery, manual judgment, and final deliverable quality.

Meet with the team Join waitlist

https://algoarena.net/oa/take/demo

Agentic assessment38:12 left

app.jsindex.html

main

1// app.js - contact form

2const form = document.querySelector("#contact-form");

3const statusNote = document.querySelector("#form-status");

6form.addEventListener("submit", (event) => {

7});

Agent

verification: behavior · code ·

Agentic

Default condition

Rena and Classic controls remain selectable

Replay

Session evidence

prompt → edit → test lineage

Competencies

one score, five transparent subscores

Pilot

Availability

guided recruiter access today

Assessment surfaces

Everything recruiters need to evaluate AI-native engineering.

Create assessments, review candidate evidence, and make hiring decisions from one recruiting workspace.

Recruiter report

Evidence-backed signal, not transcript homework.

Started

Summaries lead with one 0-100 signal, five transparent competencies, confidence, risks, and what to ask next.

Learn scoring

Recruiter report

Overall signal

0/ 100

Recommend to interview

Confidence

High

Top risk

Thin test coverage

AI fluency

Measure coding ability and AI-native workflow together.

Problem solving, planning, AI direction, verification, and agentic workflow become five readable subscores.

Read the framing

Five subscores

Problem solving88

Planning74

AI direction81

Verification69

Agentic workflow85

Pilot

Start with guided assessment pilots.

Not self-serve yet, so meet with the team to pilot assessments or join the waitlist.

Meet with the team

Guided pilot

Meet the team

Short walkthrough

Design your assessments

We tailor the work sample

Invite candidates

Send and review evidence

Frequently asked

It's active product work. Recruiter reports, candidate workflows, AI-mode controls, and replayable evidence exist, but it's not a polished self-serve product yet. Meet with the team to see where it stands.

Recent from the field notes

All field notes

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Classroom2026-07-17

An Educator Assistant That Shows Its Work

The classroom dashboard now has an assistant that drafts questions and sets up assignments, grading suggestions arrive backed by checks the platform re-runs itself, and every judgment call stays with the educator.

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Assessments2026-07-16

How to Assess Developers Who Code With AI

The old assessment measured whether you could write code unaided. That signal is collapsing. How to measure judgment when a candidate builds with AI in the loop.

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Graphs2026-07-14

DFS vs BFS: Which Graph Traversal Should You Use?

A direct comparison of depth-first and breadth-first search: traversal order, memory, shortest paths on unweighted graphs, and cycle detection, with a table and code.

Read

Get started

Pilot assessments with guided recruiter access.

Meet with the team to see recruiter reports, candidate workflows, AI-mode controls, and replayable evidence in action.

Meet with the team Assessment pricing

How we compare

Built for hiring in the AI era

Most assessment platforms grew up blocking AI, then added AI as a feature. AlgoArena starts with the real question: can this person use agents to ship trustworthy software?

	HackerRank	CodeSignal	CoderPad	Codility
Agentic work-sample assessment by default	Partial	Partial
Replayable evidence / full session replay	Partial	Partial	Partial	Partial
Keystroke & activity signals	Partial			Partial
Anti-cheat			Partial
AI workflow evidence: planning, prompting, edits, tests	Partial	Partial
Candidate-friendly (web search & AI allowed by default)			Partial