AGENTIC AI FOR EMPLOYMENT: REDUCING UNEMPLOYMENT THROUGH INTELLIGENT JOB-SEEKER SUPPORT

Abstract

Unemployment, particularly structural unemployment, remains one of the most pressing socio-economic challenges of the 21st century. While cyclical and frictional unemployment are often temporary, structural unemployment—arising from mismatches between available skills and labor market needs—persists across economies and threatens inclusive growth. Traditional responses such as Active Labor Market Policies (ALMPs), job-search counseling, and training programs have delivered mixed outcomes, as they often lag behind the rapid pace of technological and economic change.
This paper introduces the concept of the Agentic AI Employment Assistant (AAEA): a proactive, adaptive, and ethically governed artificial intelligence framework designed to support job seekers holistically. Unlike conventional job portals or applicant tracking systems that operate reactively, AAEA continuously engages with individuals by analyzing labor market dynamics, detecting skill gaps, recommending personalized microlearning, supporting job applications, and simulating interviews with contextual feedback.
The contributions of this paper are threefold: First, It conceptualizes the architecture of AAEA, integrating personalization, real-time analytics, and fairness-by-design. Secondly, it situates this framework within the academic and policy debates surrounding structural unemployment, workforce development, and AI ethics; and then It outlines implementation strategies, pilot case studies, and evaluation metrics. Findings suggest that if deployed responsibly, Agentic AI could shorten unemployment cycles, enhance employability, and increase inclusivity across diverse labor markets. The paper concludes that proactive AI assistants can transform employment support into a continuous, personalized, and equitable process.