HR AI Agent

An HR AI agent automates and assists across the employee lifecycle: sourcing candidates, screening applications, scheduling interviews, answering policy questions, onboarding new hires, predicting attrition risk, and handling routine queries. Unlike a simple HR chatbot (which answers questions), an agent takes action - booking interviews, generating job descriptions, routing benefits enquiries, and triggering onboarding workflows. The critical distinction: the agent assists human decision-makers. Every consequential employment decision - hiring, performance evaluation, termination - requires human accountability and documented rationale.

Yes - employment AI is classified as HIGH-RISK under Article 6, effective August 2, 2026. This requires: risk management systems, data governance and quality measures, technical documentation, transparency to users (candidates and employees), human oversight mechanisms, accuracy and robustness testing, and conformity assessment before deployment. Building an HR AI agent after August 2, 2026 without this architecture is non-compliant in every EU member state. We design these requirements into architecture from the first sprint.

A bias audit analyses whether your AI-powered employment tool produces disparate impact across protected categories (race, gender, age, disability). NYC Local Law 144 requires annual bias audits by independent auditors for any automated employment decision tool, with results published. The EEOC has confirmed that AI tools producing disparate impact violate Title VII regardless of intent. Even outside NYC, bias auditing is increasingly the standard of care. We build the audit infrastructure into the agent's architecture - continuous monitoring, not just annual compliance.

No. The agent handles the repetitive, high-volume work: scheduling, screening, policy queries, document processing. Your HR team handles the work that requires judgment, empathy, and relationship: interviewing, culture assessment, compensation negotiation, employee relations, and the decisions that determine someone's career. 88% of HR leaders say current AI hasn't delivered business value - often because it was positioned as replacement rather than augmentation. The value is in freeing 10-15 hours per week of your team's time from routine queries so they can invest it in the work that actually affects retention and culture.

Interview scheduling - the lowest-risk, highest-ROI starting point. Zero regulatory concern, immediate time savings, measurable improvement in candidate experience. Then employee self-service: policy Q&A grounded in your actual documents via RAG, handling the questions that consume 10-15 hours of HR time weekly. Then resume screening with bias monitoring. Then predictive attrition analytics. Start narrow, prove the ROI, expand with confidence.

Four layers. First, training data audit - identifying and mitigating bias in historical hiring data before any model learns from it. Second, model fairness testing during development - measuring disparate impact ratios, demographic parity, and equalised odds across protected categories. Third, decision-point monitoring in production - continuous bias measurement at sourcing, screening, ranking, and selection stages. Fourth, independent audit support - preparing the documentation and analysis NYC LL144 and similar laws require. Bias prevention is not a feature. It's a continuous engineering discipline.

Yes - integration with your HRIS and ATS is where the agent's actions become real. Workday, BambooHR, SAP SuccessFactors, ADP, Greenhouse, Lever, iCIMS, and Ashby are all supported. The agent reads from and writes to your system of record so every action - a scheduled interview, a moved candidate, an updated status - happens in the system your team already uses, not in a separate dashboard nobody checks.

Documented across the industry: 33% reduction in time-to-hire and cost-per-hire, $30,000-$80,000 saved per recruiter per year in time, resume screening time cut up to 75%, and 10-15 hours per week freed from routine queries. Predictive attrition saves $1,500-$3,000 per retained employee. High-impact programmes deliver 200-500% ROI in year one. But 88% of HR leaders report no significant business value - usually because measurement was never established. We define baseline metrics before building and track ROI from deployment.

Interview scheduling automation: 4-6 weeks. Employee self-service agent with policy RAG: 2-3 months. Full recruiting pipeline automation with bias compliance: 3-5 months. Predictive analytics and workforce planning: additional 2-3 months. We phase every deployment so the first use case delivers measurable value while subsequent phases are being built.

GDPR, CCPA, and local privacy regulations govern candidate data handling. We build purpose limitation (data used only for the stated hiring purpose), consent management, deletion rights (right to be forgotten after the hiring process concludes), data minimisation (collect only what's needed), and transparent processing disclosures. Candidate data is personal data, often including sensitive categories (demographics, disability status). The privacy architecture must be designed for the jurisdiction with the strictest requirements, not the most convenient one.