Agentic assessments that measure how candidates work with AI, not how well they memorized LeetCode in 2019.
OA mode is in active development. Join early access and we’ll set up a pilot when it’s ready.
Your best engineers ship with AI in the loop. Your current OA pretends that doesn't happen, and you lose the top 20% of candidates the moment they realize it.
Banning assistants and simulating 2015 doesn't make hiring fairer, it optimizes for memorization and hiding. Candidates who excel in production often lean on tools. Your signal should reflect that reality.
Strong engineers steer AI. They scope problems, write precise prompts, verify outputs, and iterate. AlgoArena captures those behaviors so you hire for how people ship, not how they perform without their stack.
In the reviewer view, open a candidate, scrub the timeline, and see prompts, tests, and decision notes.

Once you record, we’ll swap `videoSrc` in and this block becomes a real product artifact.
Specificity, context, intent, not just volume.
Refinement vs blind retries, with depth per sub-goal.
Runs after AI edits, error recovery, test discipline.
Efficiency with quality, not speed alone.
Apply-without-verify is a risk signal.
Purposeful model use across plan / code / debug.
Go beyond the final score. Watch a full replay of how the candidate approached the problem. See every keystroke, when they tabbed out, and how quickly they recovered from compilation errors, rendered as a scrubbable timeline, not a wall of logs.

Instead of banning AI and shipping spyware, we provide a built-in assistant and score how effectively the candidate collaborates with it. Blind copy-pasters are visible. Thoughtful prompters get credit for the skill they actually have.

Spin up full-stack Next.js or Python environments in the browser and ask candidates to fix a bug in a multi-file architecture, or write a unit test suite from scratch. Assess the work, not the puzzle.

They simulate a world that no longer exists, and we measure how candidates work in the one that does.
| Capability | Traditional OA | CoderPad | CodeSignal | AlgoArena |
|---|---|---|---|---|
| AI Fluency Score + dimensional radar | ✗ | ~ | ~ | ✓ |
| Multi-agent orchestration scoring | ✗ | ✗ | ✗ | ✓ |
| Session replay + AI lineage | ~ | ✓ | ~ | ✓ |
| Anti-gaming via behavior (not spyware theater) | ~ | ~ | ~ | ✓ |
| Code attribution (human / AI / hybrid) | ✗ | ✗ | ~ | ✓ |
We’re building AI-native assessments that measure how candidates actually work. If you want a pilot when it’s ready, we’ll set it up with you.
Those tools assume AI is either blocked or irrelevant. We treat it as the actual production environment. Candidates can use AI in-assessment, and we score how effectively they do. See the competitive snapshot above for specifics.
We use behavior analysis (tab focus, paste attribution, iteration patterns) instead of webcam proctoring. Cheaters exhibit measurably different patterns. It's also more respectful to candidates, and nobody loves a hiring flow that installs spyware on their laptop.
OA mode is still in development. When we open pilots, pricing will scale by candidate volume (not seats) because hiring is bursty.
Yes. You can pick from the curated library or upload your own multi-file workspace problems. Your content stays yours.
Join early access, tell us what roles you’re hiring for, and we’ll set up a pilot as OA mode matures.
Join early access and we’ll set up a pilot when OA mode is ready.
No card required to start.