Why Capital One built its multi-agent AI platform around open-weight models
Presented by Capital One
At VB Transform 2026, Kel Vanee, MVP of machine learning engineering at Capital One, spoke with Sam Witteveen, Senior Technology Contributor at VentureBeat, about how the bank built a scalable multi-agent AI architecture around deeply customized open-weight models rather than relying on an off-the-shelf foundation model.
"At Capital One, we're not just using AI, we're building AI," Vanee said.
The groundwork was laid years ago with Capital One's early investments in data transformation and cloud adoption, which Vanee said were foundational to moving quickly when the current wave of AI arrived. That technical foundation enabled the company to make several deliberate architectural decisions, including building a centralized, enterprise-wide AI platform with built-in governance, deeply customizing open models with proprietary data, and constructing its own multi-agent orchestration harness.
Customizing open-weight models with proprietary data
Rather than relying solely on off-the-shelf frontier models, Capital One fine-tunes open-weight...
Copyright of this story solely belongs to venturebeat.com. To see the full text click HERE
