# Resume Screening Agent

> A resume-screening agent that scores only job-relevant evidence, ignores protected attributes, cites every finding, and routes the decision to a human recruiter.

- **Category:** HR & Recruiting
- **Difficulty:** Advanced
- **Setup time:** 45 min
- **Stacks:** Claude, LangGraph, OpenAI
- **Version:** 2.0.0 (updated 2026-06-21)
- **Source:** https://agent-kits.com/kit/resume-screener

## Overview

Screens each resume against the role's required and preferred qualifications, with evidence cited from the resume for every met/unmet call.

Fairness-first: it scores only job-relevant criteria and never uses or infers protected attributes (age, gender, race, disability, etc.) or proxies like name, photo, or graduation year.

Human-in-the-loop by design: it recommends advance / review / decline-with-reasons but never auto-rejects — a recruiter decides.

Auditable: structured, explainable output with citations supports consistent, defensible screening.

## When to use it

- You receive high applicant volume for defined roles and want a faster, more consistent first pass.
- You have clear, job-relevant requirements (required vs. preferred) the agent can screen against.
- You need explainable, auditable assessments with evidence — for quality and compliance.
- You want to surface the strongest candidates and structured interview focus areas while a human owns every decision.

## When not to use it

- You want it to auto-reject or make final hiring decisions — it must not, and those stay with humans.
- You don't have job-relevant criteria defined, so screening would be subjective.
- You intend to screen on anything other than job-relevant qualifications — this agent explicitly won't.
- You can't keep recruiter review on outcomes or maintain an audit trail.

## Architecture

- **Role & criteria intake** — Loads the requisition's job-relevant requirements, split into required and preferred, which become the only yardstick for screening.
- **Resume parsing** — Extracts skills, experience, and accomplishments from the resume into a structured form for requirement matching.
- **Protected-attribute redaction** — Strips/ignores name, photo, and other proxy signals before scoring, so the assessment can't be driven by them even inadvertently.
- **Requirement matching** — For each requirement, the model decides met / not_met / unclear and attaches a cited evidence snippet from the resume — no evidence, no credit.
- **Fairness gate** — A guard checks that findings reference only job-relevant criteria and that no protected attribute or proxy entered the reasoning; violations are stripped and flagged.
- **Assessment & routing** — Produces the evidence-cited summary, interview focus areas, and a recommendation, then routes to a recruiter — never an auto-decision.
- **Audit & monitoring** — Logs assessments and recruiter outcomes to support audits and to monitor for adverse-impact patterns over time.

## Files in this bundle

- `system-prompt.md` — the agent's system prompt
- `tools.json` — tool/function definitions the agent expects
- `workflow.md` — step-by-step run loop
- `.env.example` — environment variables to fill in
- `kit.json` — full structured metadata
- `starters/` — minimal starter snippets
- `LICENSE` — Apache-2.0 (code, configuration, and schema)
- `LICENSE-CONTENT` — CC BY 4.0 (specification text and documentation)
- `NOTICE` — attribution and trademark notice
- `run.py` — **runnable demo**: real agent loop with mock tools (`pip install anthropic`, set `ANTHROPIC_API_KEY`, `python run.py`)
- `agentaz.json` / `agentaz.schema.json` — AgentAz™ governance spec + schema

## Implementation notes

- Make 'no auto-rejection' an enforced rule, not a preference: the agent recommends and a human decides every outcome. This is both an ethics and a compliance requirement.
- Neutralize proxies (name, photo, graduation year, address, school prestige) before scoring, and have the fairness gate strip any protected-attribute reasoning that slips into the output.
- Require cited evidence for every met/unmet finding; 'unclear' (with what's missing) is the correct answer when the resume doesn't show it — never assume.
- Apply identical criteria to every candidate for a role; consistency is what makes the process fair and auditable.
- Log assessments and recruiter outcomes and monitor for adverse-impact patterns; screening tools must be watched, not trusted blindly.
- Keep the criteria strictly job-relevant and documented per role — vague or non-job-related bars are where bias and legal risk enter.
- The strong model earns its cost on evidence-based requirement matching, while a cheaper model can parse and structure resumes.

## Variations

- **Basic — Evidence-cited screener:** Assesses a resume against the role's requirements with cited evidence and a recommendation for a recruiter. No auto-decisions; fairness guardrails on.
- **Advanced — Fair screening with audit trail:** Adds proxy neutralization, a fairness gate, interview-focus generation, and structured, auditable assessments routed to recruiters — still human-decided.
- **Enterprise — Governed, monitored screening:** Adds ATS integration, per-role criteria libraries, adverse-impact monitoring, full audit logging, and calibration from recruiter outcomes — with humans accountable for every decision.

## FAQ

**Does it reject candidates automatically?**

No. It never auto-rejects or makes a final hiring decision. It produces an evidence-cited assessment and a recommendation (advance / review / decline-with-reasons), and a human recruiter makes every call.

**How does it avoid bias?**

It scores only job-relevant criteria and is built to never use or infer protected characteristics (age, gender, race, disability, etc.) or proxies like name, photo, or graduation year. A fairness gate strips any such reasoning, and it documents that it didn't consider them.

**What if it can't tell whether a requirement is met?**

It marks the requirement 'unclear,' says what's missing, and recommends review rather than guessing met or not_met — and turns the gap into an interview question.

**Is the assessment auditable?**

Yes. Every finding cites the specific resume evidence and ties to a documented, job-relevant requirement, and assessments and outcomes are logged so the process can be reviewed and monitored for adverse impact.

**Can it just rank or auto-advance everyone to save time?**

It surfaces strong candidates with evidence, but a human reviews every assessment. The goal is faster, more consistent screening with accountability — not removing humans from the decision.

**Does it consider where someone went to school or employment gaps?**

Not as proxies for quality. School 'prestige' and unexplained gaps aren't job-relevant and can encode protected signals, so it judges demonstrated, job-relevant experience instead.


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© 2026 AgentKits · www.agent-kits.com

**Open source.** Code, configuration, and the AgentAz™ schema are licensed under
**Apache-2.0**; specification text and documentation under **CC BY 4.0** (with
attribution to AgentKits, link to https://www.agent-kits.com). See LICENSE, LICENSE-CONTENT,
and NOTICE in this bundle. "AgentAz" and "AgentKits" are trademarks of AgentKits.

**Flagship reference blueprint.** View the source, AgentAz™ spec, and frozen v1.0 schema
in the public registry: https://github.com/agent-kits/agentaz
Original: https://agent-kits.com/kit/resume-screener
