How do you balance risk management and safety with innovation in agentic systems — and how do you grapple with core considerations around data and model selection? In this VB Transform session, Milind Naphade, SVP, technology, of AI Foundations at Capital One, offered best practices and lessons learned from real-world experiments and applications for deploying and scaling an agentic workflow.
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Capital One, committed to staying at the forefront of emerging technologies, recently launched a production-grade, state-of-the-art multi-agent AI system to enhance the car-buying experience. In this system, multiple AI agents work together to not only provide information to the car buyer, but to take specific actions based on the customer’s preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent explains and validates the action plan with the user. With over 100 million customers using a wide range of other potential Capital One use case applications, the agentic system is built for scale and complexity.
“When we think of improving the customer experience, delighting the customer, we think of, what are the ways in which that can happen?” Naphade said. “Whether you’re opening an account or you want to know your balance or you’re trying to make a reservation to test a vehicle, there are a bunch of things that customers want to do. At the heart of this, very simply, how do you understand what the customer wants? How do you understand the fulfillment mechanisms at your disposal? How do you bring all the rigors of a regulated entity like Capital One, all the policies, all the business rules, all the cons …