Jacob Liberman, director of product management at NVIDIA, outlined how agentic AI bridges the gap between powerful AI models and practical enterprise applications during the NVIDIA AI Podcast. Enterprise AI adoption has evolved from early-stage chatbot implementations using large language models to intelligent multi-agent systems that reason, act and execute complex tasks with autonomy.
Enterprises deploy AI agents to free human workers from time-consuming and error-prone tasks, allowing people to focus on high-value work requiring creativity and strategic thinking. Liberman anticipates teams of AI agents and human workers will collaborate on complex tasks requiring reasoning, intuition and judgment, with enterprise software developers working with AI agents to develop more efficient algorithms and medical researchers collaborating with AI agents to design and test new drugs.
NVIDIA AI Blueprints help enterprises build AI agents for various use cases through reference architectures implemented in code. "Blueprints are reference architectures implemented in code that show you how to take NVIDIA software and apply it to some productive task in an enterprise to solve a real business problem," Liberman explained.
The blueprints are entirely open source, enabling developers or service providers to deploy directly or customise by integrating their own technology. Liberman highlighted the AI Blueprint for customer service featuring digital humans, noting versatility in applications. "The digital human can be made into a bedside digital nurse, a sportscaster or a bank teller with just some verticalisation," he said.
Popular NVIDIA Blueprints include video search and summarisation agent, enterprise multimodal PDF chatbot, and generative virtual screening pipeline for drug discovery. The podcast covered AI agent definitions, software developer adoption of agentic AI, test-time compute and reasoning models explanation, and AI agent applications in cybersecurity and risk management.
Organisations can leverage NVIDIA AI Blueprints to develop customised multi-agent systems addressing specific business challenges while maintaining open-source flexibility. The reference architectures enable enterprises to accelerate AI agent deployment across customer service, cybersecurity, and drug discovery applications through proven implementation frameworks.
Enterprise agentic AI adoption represents a strategic shift from basic chatbot functionality to autonomous task execution systems. Organisations implementing multi-agent collaboration can achieve operational efficiency gains by automating complex workflows while enabling human workers to focus on strategic initiatives. NVIDIA's open-source blueprint approach reduces implementation barriers and accelerates enterprise AI transformation. The evolution toward human-AI agent collaboration creates competitive advantages for organisations investing in advanced AI capabilities across software development, medical research, and customer service operations.