NVIDIA’s Bartley Richardson on How Teams of AI Agents Provide Next-Level Automation

Building effective agentic AI systems requires rethinking how technology interacts and delivers value across organizations.

Bartley Richardson, senior director of engineering and AI infrastructure at NVIDIA, joined the NVIDIA AI Podcast to discuss how enterprises can successfully deploy agentic AI systems.

“When I talk with people about agents and agentic AI, what I really want to say is automation,” Richardson said. “It is that next level of automation.”

Richardson explains that AI reasoning models play a critical role in these systems by “thinking out loud” and enabling better planning capabilities.

“Reasoning models have been trained and tuned in a very specific way to think — almost like thinking out loud,” Richardson said. “It’s kind of like when you’re brainstorming with your colleagues or family.”

What makes NVIDIA’s Llama Nemotron models distinctive is that they give users the ability to toggle reasoning on or off within the same model, optimizing for specific tasks.

Enterprise IT leaders must acknowledge the multi-vendor reality of modern environments,  Richardson explained, saying organizations will have agent systems from various sources working together simultaneously.

“You’re going to have all these agents working together, and the trick is discovering how to let them all mesh together in a somewhat seamless way for your employees,” Richardson said.

To address this challenge, NVIDIA developed the AI-Q Blueprint for developing advanced agentic AI systems. Teams can build AI agents to automate complex tasks, break down operational silos and drive efficiency across industries. The blueprint uses the open-source NVIDIA Agent Intelligence (AIQ) toolkit to evaluate and profile agent workflows, making it easier to optimize and ensure interoperability among agents, tools and data sources.

“We have customers that optimize their tool-calling chains and get 15x speedups through their pipeline using AI-Q,” Richardson said.

He also emphasized the importance of maintaining realistic expectations that still provide significant business value.

“Agentic systems will make mistakes,” Richardson added. “But if it gets you 60%, 70%, 80% of the way there, that’s amazing.”

Time Stamps

1:15 – Defining agentic AI as the next evolution of enterprise automation.

4:06 – How reasoning models enhance agentic system capabilities.

12:41 – Enterprise considerations for implementing multi-vendor agent systems.

19:33 – Introduction to the NVIDIA Agent Intelligence toolkit for observability and traceability.

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