CVS Health and Aible are Delivering Enterprise AI with Rapid Prototyping, Agents, and Reasoning Models - Ep. 261
Episode
39 min
Read time
2 min
Topics
Productivity, Health & Wellness, Design & UX
AI-Generated Summary
Key Takeaways
- ✓Rapid Prototyping Framework: Implement forty-eight hour maximum prototype cycles from project approval to business stakeholder review, followed by thirty-day deployment to production scale. This prevents projects from stalling in endless planning phases where stakeholders forget original requirements.
- ✓Reasoning Models with Feedback: Force AI models to explain their reasoning step-by-step, then provide feedback on specific reasoning steps rather than just final outputs. This approach requires significantly less feedback to improve model performance and gives users immediate payoff from corrections.
- ✓Agent Evolution Strategy: Start with general intern-level AI agents that evolve into thousands of specialized agents through user feedback on specific use cases. Each agent learns domain terminology, company processes, and user preferences through continuous fine-tuning every one hundred observations.
- ✓Deterministic-Probabilistic Balance: Use generative AI for language and synthesis tasks while routing mathematical calculations, data analysis, and auditable operations to deterministic systems through tool calling. This ensures accuracy for regulated healthcare environments while maintaining conversational interfaces for users.
What It Covers
CVS Health and Aible demonstrate enterprise AI implementation using rapid prototyping, reasoning models, and agentic systems on NVIDIA DGX Cloud, focusing on forty-eight hour prototypes and thirty-day deployment cycles for healthcare applications.
Key Questions Answered
- •Rapid Prototyping Framework: Implement forty-eight hour maximum prototype cycles from project approval to business stakeholder review, followed by thirty-day deployment to production scale. This prevents projects from stalling in endless planning phases where stakeholders forget original requirements.
- •Reasoning Models with Feedback: Force AI models to explain their reasoning step-by-step, then provide feedback on specific reasoning steps rather than just final outputs. This approach requires significantly less feedback to improve model performance and gives users immediate payoff from corrections.
- •Agent Evolution Strategy: Start with general intern-level AI agents that evolve into thousands of specialized agents through user feedback on specific use cases. Each agent learns domain terminology, company processes, and user preferences through continuous fine-tuning every one hundred observations.
- •Deterministic-Probabilistic Balance: Use generative AI for language and synthesis tasks while routing mathematical calculations, data analysis, and auditable operations to deterministic systems through tool calling. This ensures accuracy for regulated healthcare environments while maintaining conversational interfaces for users.
Notable Moment
Sengupta reveals that when AI agents communicate with each other to negotiate solutions, they often abandon English entirely and create their own languages to exchange information more efficiently, an emergent capability researchers never explicitly programmed.
Episode Transcript
Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. Our guests today have known and worked with one another for years and can speak to the ins and outs of enterprise technology, including developing AI applications from the perspective of brands, vendors, developers, customers, and probably most anyone else you can think of. Tony Ambrosi is senior vice president and chief digital and technology officer of pharmacy and consumer wellness for CVS Health, and Orijit Sengupta is founder and CEO of Able. Tony and Orijit are here to talk about developing AI apps at massive scale using DGX Cloud to do it and the current and future of generative AI, Genentech AI frameworks, and so much more. So I'm gonna get out of the way and introduce these two gentlemen to share some tales and drop some knowledge on us in the next half an hour. Tony, Urujeet, thank you so much for joining the AI podcast. Well, thank you for inviting us. So if you would, maybe we can start a little bit with your backgrounds and kind of how you've been working together forever. So I'll let you kind of start at the point that seems apt to get us into what CVS and Abel are doing now. So I'll I'll, I'll start. So I've been doing this for technology and digital and then at some point data, machine learning for quite a while, trying to be at the forefront of technology to support customers and, employees and colleagues and whoever else there. I've spent my last five years in health care, three years in in a hospital system, South Florida, obviously focused on patients and patients', well-being. I've been with the with CVS for, almost two years now, the same type of focus. And I think Arjith and I kind of bumped into each other 2016, 2017, I think, year. And then we saw that that it would be cool to do interesting things with this machine learning thing because we can transform a lot of make a lot of things better. At that time, I was with Disney, and, make a lot of things much better for consumers and and guests. And so, Arjji, where were you when you guys met? So I had, right before that, sold my previous AI company to Salesforce, where it became Salesforce Einstein Discovery. So I started to build BeyondCore, which, was one of the first augmented analytics companies on the planet and ended up having a crisis of faith. I was like, did I create value, or did I just make some money? Right? Right. So I ended up writing a book called AI is a waste of money, where I've taken a thousand AI projects, and I laid out why these projects fail. And one of the most common reasons was disconnect between the business user and data scientists. Mhmm. And this is where Tony and I always had this fun thing, which was …
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