For engineering and hiring teams
See whether candidates can build, verify AI output, catch important mistakes, and explain what deserves trust. Then give every reviewer the evidence behind the result.
Before the timer
Candidate preflight and rehearsal
During the work
Executable tasks and AI judgment
After submission
Human-reviewed evidence and live prompts
The hiring workflow
CodeArena organizes the screen around the decision your team must make: what the candidate built, how they handled uncertainty, and what still needs a human follow-up.
Start with the work the role needs: implementation, debugging, prompting, AI Critique, verification, or quality-gate judgment.
JD analysis suggests the screen brief, skills, interview questions, and scorecard criteria; your team still chooses the tasks and approves the final screen.
Executable results, seeded defects, candidate decisions, reasoning, and available session context stay attached to the submission.
Judgment-graded work remains pending review until the evidence is ready for a person to inspect.
Reports name the uncertainty that deserves follow-up instead of sending interviewers into another generic coding round.
Use the prompts in CodePair or the live interview workflow your team already trusts.
Role coverage
Use a role pack as the first draft, then tune the mix of coding, prompting, AI Critique, verification, and quality-gate work before inviting candidates.
UI implementation, state-shaped data, JavaScript fluency, and AI-output review.
AI-code critique, bug verification, prompting discipline, and core implementation.
Incident response, observability, CI/CD decisions, and reliability communication.
Customer translation, technical execution, API reasoning, and stakeholder clarity.
Threat reasoning, secure implementation, incident judgment, and privacy risk.
Data-shaped coding, Python correctness, experiment analysis, and critique of generated output.
Candidate experience
Better evidence starts with clear expectations, resilient work state, and an interface candidates can understand before the result counts.
Time, task types, AI policy, monitoring status, and submission expectations are disclosed before assessed work begins.
AI Critique screens include an unscored Trust or Challenge rehearsal so candidates can practice the fixed verification budget before the timer.
Draft and resume protections reduce the chance that a refresh or network interruption erases a candidate session.
Submission receipts confirm completion. After human review, teams can share candidate-safe strengths and development areas.
Discernment in practice
This is the core move: inspect generated work, find the important defect, avoid false alarms, and explain what should happen next.
See how Discernment worksTry the move yourself
This is the skill CodeArena screens for: can an engineer tell when confident AI output is wrong before it ships? Your reviewers see who can, with evidence.
AI assistant: “Handles rounding safely and avoids floating-point drift. Ready to merge.”
The tests pass and the AI is confident. One line still ships a money bug. Click the line you would flag in review.
Reviewer evidence
Reviewers see what ran, what failed, which AI claims were challenged, which signals still need review, and what question should be asked next.
Senior frontend signal pack
Test outcomes, runtime behavior, failed attempts, and final code where the task supports execution.
Trust or Challenge decisions, seeded-defect outcomes, false-positive control, and suspicion allocations where captured.
Pending-review guards, reviewer notes, and explicit ownership keep automated summaries from becoming silent hiring decisions.
The report converts specific misses or uncertainty into prompts for live validation.
Fits the hiring stack
Business includes session replay with available integrity-event context.
Bulk invites and role-gated reviewer actions support repeat hiring loops.
Branded reports, reviewer exports, and candidate-safe shareback keep decisions legible.
Business includes Greenhouse, Lever, and Ashby sync and write-back.
Buyer questions
CodeArena should make the evidence easier to inspect, not hide uncertainty behind product language.
Yes when the assessment is configured for AI use. The policy is disclosed before the candidate starts. CodeArena then captures how the candidate directs, challenges, and verifies AI output instead of pretending AI is absent.
Executable checks and seeded defects provide known-answer evidence where available. AI-assisted grading can organize reasoning, but judgment-heavy results remain reviewable and humans own the hiring decision.
It summarizes available evidence about catching real defects, avoiding false alarms, and expressing uncertainty through candidate decisions. It is one narrow input, not a complete verdict on an engineer.
CodeArena records available session and integrity events for contextual review. Those events are evidence, not automatic proof of misconduct, and the product does not score faces, voices, emotions, or personality.
Yes. Starter is designed for one real hiring loop with 10 candidate sessions per month. Business adds repeat volume, replay, bulk invites, ATS integrations, and expanded reporting.
Start a Starter workspace now, or request a 15-minute walkthrough to map the signal, candidate path, evidence packet, and focused live validation plan.