Business processes have become more complicated because organizations need approval systems that can handle their requirements. The standard way of approving requests works through a system of established rules which needs human input but creates problems because it causes work delays and makes it difficult to work with changing conditions while there exists a strong chance that staff members will make mistakes. The organization cannot work efficiently because these boundaries prevent work from finishing which results in crucial decisions taking more time than necessary. This research presents an integrated solution which combines Agentic Artificial Intelligence (AI) with advanced data analytics to create automated approval systems that make decisions based on data and contextual information. Agentic AI refers to intelligent systems that can act independently, pursue defined goals, and continuously learn from their environment. The combination of these systems with data analytics enables them to process extensive historical and current business information which they use to discover patterns and forecast results while making educated approval choices. The proposed framework incorporates machine learning algorithms, natural language processing techniques, and reinforcement learning models to enhance system adaptability and decision accuracy across various enterprise domains, including finance, procurement, and human resource management. The study uses a mixed-method research approach which evaluates system performance through qualitative methods and analyzes workflow data through quantitative methods. The researchers evaluated key performance indicators which included processing time and decision accuracy and consistency and user satisfaction to determine how well the proposed system performed. The study shows that agentic AI combined with data analytics reduces approval processing time while improving decision-making consistency and making system operations more transparent. The system can learn from its previous decisions which enables it to enhance its performance throughout its operational lifespan. The study shows that AI-based automated approval systems provide enterprises with workflow improvements which create quicker and more trustworthy decision-making processes through automatic resource management and reduced need for human work. The study presents practical organizational benefits to digital transformation efforts while it proposes future research which needs explainable AI and multi-agent systems to improve enterprise automation trust and accountability and scalability.