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AI is rapidly transforming how organizations solve complex challenges.
The early stages of enterprise AI adoption focused on using large language models to create chatbots. Now, enterprises are using agentic AI to create intelligent systems that reason, act and execute complex tasks with a degree of autonomy.
Jacob Liberman, director of product management at NVIDIA, joined the NVIDIA AI Podcast to explain how agentic AI bridges the gap between powerful AI models and practical enterprise applications.
Enterprises are deploying AI agents to free human workers from time-consuming and error-prone tasks. This allows people to spend more time on high-value work that requires creativity and strategic thinking.
Liberman anticipates it won’t be long before teams of AI agents and human workers collaborate to tackle complex tasks requiring reasoning, intuition and judgement. For example, enterprise software developers will work with AI agents to develop more efficient algorithms. And medical researchers will collaborate with AI agents to design and test new drugs.
NVIDIA AI Blueprints help enterprises build their own AI agents – including many of the use cases listed above.
“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 said.
The blueprints are entirely open source. A developer or service provider can deploy a blueprint directly, or customize it by integrating their own technology.
Liberman highlighted the versatility of the AI Blueprint for customer service, for example, which features digital humans.
“The digital human can be made into a bedside digital nurse, a sportscaster or a bank teller with just some verticalization,” he said.
Other popular NVIDIA Blueprints include a video search and summarization agent, an enterprise multimodal PDF chatbot and a generative virtual screening pipeline for drug discovery.
1:14 – What is an AI agent?
17:25 – How software developers are early adopters of agentic AI.
19:50 – Explanation of test-time compute and reasoning models.
23:05 – Using AI agents in cybersecurity and risk management applications.
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