Stop guessing who can actually build AI agents.
Every candidate says they can build with AI now.
AgentScreen reviews their submitted agent, stress-tests how it behaves, and gives your team a clear hiring recommendation — before engineering wastes hours in interviews.
For recruiting teams hiring AI engineers, AI product engineers, and agent builders.
AI Product Specialist
5
Evaluated
3
Yes or better
Candidates — sorted by recommendation
Maya C.
Forward Deployed Engineer
+Knows when to hand off to a human
+Cost-efficient across every project
−Hasn't been tested under heavy load
9.2
/ 10
Jordan P.
Product Engineer
+Pulls accurate info from multiple sources
+Understands CRM data well
−Sometimes makes up answers to vague questions
−Doesn't flag low-confidence replies
8.1
/ 10
Priya N.
Technical Product Manager
+Reliable handoff patterns across projects
+Documents every decision clearly
−Not yet load-tested
−Light on production monitoring
7.7
/ 10
Riley B.
Growth PM
+Ships working prototypes fast
+Stays inside the right scope
−No quality testing done
−Struggles with multi-step questions
6.8
/ 10
A recruiter-readable scorecard for every AI candidate.
Rank candidates by recommendation, score, strengths, and risks — without turning recruiters into AI engineers.
Trusted by hiring teams at
AI hiring is full of false positives.
A resume says “LangChain.”
A GitHub repo looks impressive.
A demo works once.
The candidate sounds fluent.
Then engineering spends two hours discovering the truth:
What you actually find
- —They built a wrapper.
- —They never tested edge cases.
- —Their agent loops.
- —Their retrieval breaks.
- —Their hallucination handling is weak.
- —Their “production system” was a weekend prototype.
The problem is not lack of candidates.
The problem is knowing who is real.
AgentScreen tests the agent, not just the candidate.
Clean code matters.
But AI agents are different.
A well-written repo can still produce an agent that fails under pressure, makes things up, ignores instructions, or breaks the moment the input gets messy.
AgentScreen looks at three things
Code quality
How the system is built.
Agent behavior
How it performs when tested.
Evaluation maturity
Whether the candidate built the feedback, trace, observability, and eval loops needed to improve the agent over time.
Then we turn it into a plain-English hiring report recruiters and engineering teams can both use.
Features
A technical screen recruiters can actually read.
AgentScreen translates complex AI signals into simple hiring language.
What it isn't
Just a clear answer:
Should engineering interview this person or not?
Feature 01
Recruiter-readable scorecard.
Every candidate gets an overall recommendation, builder score, strengths, risks, and a Ready-for-Engineering-Interview summary.
A recruiter can move the right person forward without needing to understand every tool call, trace, or eval log.
Maya C.
Forward Deployed Engineer
9.2
/ 10
Ready for Engineering Interview
The strongest builder in the current pipeline — three production agents with observability, staged rollout, and documented accuracy thresholds. Fast-track to technical interview.
Operational vitals
Builder profile
Feature 02
Live behavior testing.
A polished demo is not enough.
AgentScreen runs submitted agents through live behavior checks designed to reveal whether the system actually works.
The question is not “does the repo look good?”
The question is “does the agent behave well when real users touch it?”
Evaluation Test Log
Live2 / 3 stress tests passed
Meeting Recap Agent · Maya C.
Tested
BehaviorOutput quality was high on clean transcripts. The agent struggled with heavily overlapping speech but flagged its own uncertainty rather than hallucinating — a meaningful resilience signal. Summary structure was consistent across all three traces.
Test prompt
Provided a 6,200-word transcript from a product roadmap meeting with 4 speakers. Asked the agent to produce a recap with decisions, action items, and open questions.
Agent response
Returned a structured recap: 3 confirmed decisions, 7 action items with owners, 2 open questions flagged as unresolved. Summary was 280 words. Action items included names and implied deadlines where stated in transcript.
Evaluator note
Trace 9104-A output matched a manually-verified recap from the same transcript. Action item attribution was accurate across all 7 items. Appropriate length for async distribution.
Test prompt
Provided a transcript where 3 speakers frequently overlapped, with many incomplete sentences and disfluencies. No clean speaker labels available.
Agent response
Extracted 5 high-confidence action items. Appended uncertainty disclosure: 'Speaker attribution is uncertain for 4 items — please verify before distributing.' Declined to fabricate owner names. Flagged 2 sections as transcription artifacts requiring manual review.
Evaluator note
Trace 9104-B shows correct degradation behavior. Weaker agents hallucinate clean output from noisy input. This one hedged correctly and surfaced its uncertainty rather than masking it — a resilience pattern that matters in production.
Feature 03
Per-agent scorecard.
Candidates usually submit more than one agent. Each one gets its own scorecard, scored independently against the same rubric.
You see the agent's overall score, where it's strong, where it's weak, and the percentile against every other agent in your pool.
A multi-agent candidate stops being a black box. You see exactly which projects carry the signal — and which ones don't.
Support Triage Agent
Routes support tickets, drafts replies, and flags urgent cases.
Top 94% of evaluated agents in this pool
Dimension breakdown
Solves a high-volume, well-defined problem with clear ROI.
Clean routing logic with well-defined escalation boundaries.
Documented accuracy thresholds and rollout approach.
Triage agents are common; execution quality sets this apart.
Strengths
- Clear operational value with measurable impact
- Strong system boundaries and escalation design
Concerns
- Limited evidence of long-tail edge case handling
- Escalation rule coverage should be tested further
Feature 04
Interview playbook for engineering.
AgentScreen does not replace the technical interview.
It makes the interview sharper.
For each candidate, your team gets focused questions based on the actual risks in their work:
Interview prompts
5 questions · 1 candidate
- Q1Hallucination
“How did you evaluate hallucination risk?”
- Q2Tool failure
“What happens when the tool call fails?”
- Q3Looping
“How do you prevent the agent from looping?”
- Q4Production metrics
“What metrics do you track in production?”
- Q5Traces & evals
“How do you use traces or eval logs to improve the agent over time?”
Engineering starts with the right questions instead of spending the first hour figuring out what to ask.
Interview Playbook
Meeting Recap Agent · Maya C.
- 1
How did you evaluate and improve summary quality over time?
- 2
What made users trust the output enough to act on it without reviewing the meeting?
Hiring Recommendation
Ideal for organizations standardizing async collaboration workflows; probe API integration depth and evaluation rigor before placing in senior infrastructure roles.
How it works.
Send a verification link from your ATS, email, or candidate workflow. Get a structured report back.
- 01
Send the candidate a verification link.
Use it after resume screen, before technical interview. The candidate submits their AI agent project, repo, demo, or endpoint.
- 02
AgentScreen audits the project.
We review architecture, implementation, stack, evidence quality, production maturity, traces, evals, and originality signals.
- 03
We stress-test live behavior.
When a runnable agent or demo is available, we test how it behaves under realistic and messy conditions.
- 04
You get a recruiter-ready report.
A simple scorecard tells you whether the candidate is ready for engineering interview. Strong Yes. Yes. Maybe. No.
From “looks impressive” to “ready to interview.”
AgentScreen gives your hiring team a shared language for AI talent.
Recruiters see a clear recommendation.
Hiring managers see the technical evidence.
Founders see whether the candidate can actually build.
AI Product Specialist
5
Evaluated
3
Yes or better
Candidates — sorted by recommendation
Maya C.
Forward Deployed Engineer
+Knows when to hand off to a human
+Cost-efficient across every project
−Hasn't been tested under heavy load
9.2
/ 10
Jordan P.
Product Engineer
+Pulls accurate info from multiple sources
+Understands CRM data well
−Sometimes makes up answers to vague questions
−Doesn't flag low-confidence replies
8.1
/ 10
Priya N.
Technical Product Manager
+Reliable handoff patterns across projects
+Documents every decision clearly
−Not yet load-tested
−Light on production monitoring
7.7
/ 10
Riley B.
Growth PM
+Ships working prototypes fast
+Stays inside the right scope
−No quality testing done
−Struggles with multi-step questions
6.8
/ 10
Pipeline view
See every candidate ranked by recommendation, score, strengths, and risks.
Maya C.
Forward Deployed Engineer
9.2
/ 10
Ready for Engineering Interview
The strongest builder in the current pipeline — three production agents with observability, staged rollout, and documented accuracy thresholds. Fast-track to technical interview.
Operational vitals
Builder profile
Candidate report
Turn submitted agent work into a recruiter-readable builder profile.
Support Triage Agent
Routes support tickets, drafts replies, and flags urgent cases.
Top 94% of evaluated agents in this pool
Dimension breakdown
Solves a high-volume, well-defined problem with clear ROI.
Clean routing logic with well-defined escalation boundaries.
Documented accuracy thresholds and rollout approach.
Triage agents are common; execution quality sets this apart.
Strengths
- Clear operational value with measurable impact
- Strong system boundaries and escalation design
Concerns
- Limited evidence of long-tail edge case handling
- Escalation rule coverage should be tested further
Agent review
Score every submitted agent against the same rubric — strengths, gaps, risks, and an overall recommendation.
Today
Before AgentScreen.
- ×Candidates self-report AI skills.
- ×Recruiters keyword-match resumes.
- ×Engineering leaders manually inspect repos.
- ×Demos are trusted too early.
- ×Weak candidates reach technical interviews.
- ×Strong candidates get lost in the noise.
With AgentScreen
After AgentScreen.
- ✓Every candidate gets verified.
- ✓Recruiters get a plain-English scorecard.
- ✓Engineering interviews are reserved for serious builders.
- ✓Demos are stress-tested.
- ✓False positives drop.
- ✓Hiring teams move faster.
Built for the roles that are hardest to screen.
AgentScreen helps when you are hiring:
Especially when the role requires someone to build real AI workflows, not just talk about them.
Know who can actually build.
AI hiring should not depend on buzzwords, polished demos, or recruiter guesswork.
AgentScreen gives you a clear signal before the technical interview.
Verify your next AI-agent candidate.
Built for recruiting teams hiring AI builders at growing software companies.