Abstract: The rapid deployment of AI agents in social science research—systems that orchestrate multi-step workflows with persistent memory, tool access, and domain expertise—marks a fundamental shift in how scholarly knowledge is produced. This article examines the organizational and individual implications of this transformation through the lens of work redesign, drawing on evidence from recent empirical studies, operational AI research systems, and labor economics frameworks. AI agents excel at codifiable execution tasks but struggle with tacit judgment, creating a "jagged technological frontier" where capability boundaries are unpredictable. This delegation boundary cuts through every stage of the research pipeline rather than between stages, requiring researchers to maintain verification capacity even as they delegate production. The article identifies three critical challenges: maintaining oversight capacity amid progressive automation (the augmentation-to-dependency slide), managing stratification in access to AI productivity tools, and preserving apprenticeship pathways in graduate training. Evidence-based organizational responses include deliberate workflow mapping, parallel competence maintenance, protected training environments, and transparency protocols. The article concludes that productive augmentation depends on researchers retaining authorship of theoretical contributions and judgment-intensive decisions while delegating codifiable execution—a fragile equilibrium requiring institutional support, pedagogical innovation, and normative clarity about disclosure and verification standards.
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