Artificial intelligence (AI) agents capable of autonomous decision-making are increasingly transforming business processes and managerial decision structures. Despite growing adoption, research remains fragmented across domains and lacks a unified understanding of how agentic AI is conceptualized, applied, and governed in business contexts. This study conducts a systematic scoping review of literature published between 2020 and 2025 using major academic databases. A total of 875 studies were included for quantitative mapping and thematic analysis, with a representative subset selected for in-depth qualitative synthesis. Findings reveal three dominant conceptual lenses of AI agents (technical, organizational, and hybrid), with applications concentrated in strategy, operations, and human resource management. Reinforcement learning, simulation, and optimization are the most commonly used techniques. While AI agents contribute to efficiency gains and faster decision-making, significant challenges related to accountability, transparency, and system integration remain. We propose an Agentic AI in Business (AAB) integration model and a taxonomy distinguishing decision-support, semi-autonomous, and fully autonomous systems. The study offers conceptual clarity and practical insights for organizations and policymakers regarding governance, trust, and responsible adoption of autonomous AI systems. Policymakers should develop differentiated governance frameworks that calibrate regulatory oversight to the level of AI autonomy, ensuring accountability without impeding innovation.