When algorithms become employees: reframing learning and development in the age of agentic AI

Purpose This study examines agentic artificial intelligence (AI) as “digital employees” and extends learning & development (L&D) frameworks to include their training and ethical alignment. Design/methodology/approach A conceptual, theory-integrative approach is adopted, drawing on socio-technical systems theory and the resource-based view (RBV), supported by evidence such as bias on Airbnb. Findings Agentic AI systems act as organizational participants whose learning shapes decisions. Reframing their learning as an L&D responsibility requires core L&D activities to be rethought for non-human learners. Without governance, these systems may amplify bias. Research limitations/implications The study is conceptual and lacks empirical validation. Future research should test AI-focused L&D models and examine human-AI collaboration. Practical implications The study can be used to implement structured AI training, align systems with ethical norms, and strengthen governance for responsible AI use in organizations. Originality/value The paper reframes agentic AI as part of the workforce and extends L&D to include artificial agents, clarifying what the L&D function specifically gains from this perspective.