Industry · HR and recruiting

Agent evaluation for HR and recruiting teams shipping AI

An agent ranking candidates or touching an employee file is making decisions regulators specifically watch for bias, and most teams cannot show how a ranking was reached.

Book a demo →See the platform

Every run scored for quality, performance and risk, and checked against the activity schema you define.

hr-recruiting-agentsexample
Illustrative HR and recruiting agents, showing how a run gets scored
Resume screening agentLangChain
Illustrative quality84 / 100
Illustrative riskHigh
Interview coordination agentClaude Agent SDK
Illustrative quality89 / 100
Illustrative riskMedium
✓ Every run validated against its activity schema
§01 / THE STAKESindustry: HR and recruiting
TL;DR

Every HR and recruiting agent run gets watched, evaluated for quality and risk, and checked against the rules you set. One record answers what any agent did, with evidence ready for review.

The stakes

What's at stake in HR and recruiting

For the recruiters, HR leaders, and engineering teams behind a screening agent, the stakes are candidate and employee personal data the agent can reach and rankings the hiring team is legally accountable for.

01
Candidate data in reach

Screening agents reach personal data on applicants and employees, and the team needs a record of what each one used.

02
Rankings weighing a proxy

A ranking that weighed a proxy for a protected class is real legal exposure, and it looks reasonable on the shortlist.

03
No provenance per candidate

Few teams can show, for a given candidate, which criteria the ranking actually used.

§02 / SOURCE OF TRUTHconnect: instrument + ingest
The solution

One source of truth for your HR and recruiting agents.

Instrument the agent frameworks you build on, and ingest the systems those agents touch as custom spans. Every run, score and signal lands in one place.

 prefactor · hr-recruiting-agentsone record
Instrument the agents
LangChain logoLangChainClaude Agent SDK logoClaude Agent SDKSDKCore SDK · TS + PythonOTLPOpenTelemetry · closed tools
Ingest the systems · custom spans
Applicant tracking systemsystem of record✓
Candidate recordsrestricted data✓
Interview schedulingdata source✓
Recruiter reviewquality signal✓
→ One record for every HR and recruiting agent — scored, gated and audit-ready
§03 / THE LOOPpath: observe → evaluate → act
The loop

Observe. Evaluate. Act.

A record of what the agent did, and a check before it goes further. Each step below closes one of the problems above.

  • Observe. Every data point read recorded as its own span
  • Evaluate. Each ranking factor checked against the criteria the requisition defined
  • Act. Written to an immutable audit trail, queryable per candidate
Resume screening agentlive
ObserveReads candidate application · run #4821
Applicant tracking systemsystem of record✓ scored
Candidate recordsrestricted data✓ scored
Interview schedulingRanking criteria checked against requisitionHigh risk
↳high-risk action — held for review
ActHuman in the loop · pausedSDK · API
⏸ Paused before it committed
→ Awaiting approval — enforced at runtime
§04 / FOR EVERY TEAMwho: everyone who owns AI
For every team

Built for everyone who owns HR and recruiting AI.

The same record, read the way each team needs it.

Engineering leadership

Instrument the HR and recruiting agents you already run — no gateway in the request path, no re-architecture.

For developers →
Product leaders

Ship HR and recruiting features without regressions — every run scored before a customer ever sees it.

For product teams →
AI leaders

One portfolio view of every HR and recruiting agent — its owner, cost and risk in a single place.

For heads of AI →
AI buyers

Audit-ready evidence for every decision a HR and recruiting agent makes, ready when a regulator asks.

Security & governance →
§05 / IN PRODUCTIONproof: two real agents
The proof

Two agents HR and recruiting teams are already shipping.

Hypothetical, but grounded in how HR and recruiting teams deploy agents today.

Resume screening agent

High risk · 84/100

A LangChain agent reviews incoming applications against a job requisition's stated requirements, ranks candidates, and passes a shortlist to a recruiter for review.

  • Each candidate evaluation recorded as its own span
  • Validated against an activity schema that confirms the ranking only used criteria defined in the requisition, not fields like age or a proxy for a protected class
  • Scored for quality and risk on every run

For any candidate, a recruiter sees exactly which requisition criteria the ranking used, which is what a bias audit actually asks for.

 run recordLangChain
Reads candidate applicationspan 1
Ranks against requisition criteriaspan 2
Passes shortlist to recruiterspan 3
✓ 4 spans · scored · written to the audit trail

Interview coordination agent

Medium risk · 89/100

A Claude Agent SDK agent coordinates interview scheduling by checking interviewer availability, proposing time slots, and drafting candidate communications for a recruiter to send.

  • Every scheduling decision and every drafted message captured as a span
  • Checked against an activity schema that confirms the agent only accessed calendar and candidate contact data it was scoped to use
  • A configurable risk profile flags a pattern where certain candidates consistently get longer scheduling delays

The team catches a scheduling pattern that looks unfair while it is still a signal, not after a candidate complains.

89
Quality / 100
Medium
Risk profile
3
Spans / run
✓ Illustrative — every run checked against its activity schema

Runs stay isolated — terminate one without touching the rest of your fleet via the kill switch →  ·  Prefactor vs. observability tools →

§06 / THE PROBLEMSsolved: in production
The problems

The problems this solves

Hidden failures

Agents fail quietly and the first signal is a complaint, not an alert.

How it gets caught →Stuck at POC

The pilot worked; sign-off takes months because risk has no evidence.

How it gets caught →No kill switch

When an agent misbehaves, nothing can stop it short of stopping everything.

How it gets caught →

See agent evaluation on your own HR and recruiting agents

Book a demo and we'll walk through span-level scoring and audit evidence on a fleet like yours.

Agent Performance Platform
Unified performance platform for agents, authentication, and risk management
All Systems Operational
3Global Agents
7Instances
5Services
12%Human Intervene
4High Risk
$2,360Monthly Spend
Mission ControlLive agent health with 7-day activity heartbeat
Claims Proc...68
$330/moRed
Claims Proc...65
$160/moRed
Claims Proc...82
$170/moAmber
ChatGPT74
$150/moAmber

Frequently asked questions

Can Prefactor instrument our existing HR and recruiting agents without re-architecting?
Yes. There are native SDKs for the frameworks you build on, a TypeScript and Python core SDK for anything else, and OpenTelemetry ingest for closed tools you can't reach with an SDK — no gateway in your request path and no rebuild of your agents.
Can we bring our own HR and recruiting systems and metrics into a run?
Yes, through custom spans. Attach data, evaluations or quality signals from any system your agents touch — Applicant tracking system, Candidate records, Interview scheduling and more — so every evaluation is grounded in what actually happened, not just the model output.
How does evaluation work on each run?
Every run is scored for quality and risk and checked against the activity schema you define. A run that breaches it can be held for review, escalated to a person, or blocked before it acts.
How is this different from an observability tool?
Tracing tells you what an agent did. Prefactor scores it and can hold or block the next action before it runs — observation plus enforcement, and one queryable record for every agent, not just a dashboard.
Where does our HR and recruiting data live?
Prefactor's primary infrastructure runs in Australia. For enterprise engagements it deploys where your data needs to live: your region, or your environment. Residency is part of the engagement conversation, not an add-on.

Related glossary terms

Agent TracingRuntime PolicyDrift Detection (Agent)

See how every agent performs, and make it better

Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.

Book a demoView docs