Researchers and industry leaders are advancing biomanufacturing through agentic AI and digital twin technologies to overcome current limitations. Systems like SIC (sense, infer, and control) integrate sensor data with AI agents and process simulations for autonomous real-time optimization of complex biological processes. Meanwhile, Seoul National University scientists improved digital twins by combining varied data types and analyzing critical quality attributes in monoclonal antibodies, enhancing model accuracy for manufacturing control. These developments promise increased efficiency, precision, and cost-effectiveness in biopharmaceutical production amidst fragmented data and scaling challenges.