Cohere VP: Enterprise AI sovereignty requires control of full agent stack

Hundreds of enterprise leaders and technical experts gathered at VB Transform 2026 in Menlo Park for a discussion on using generative AI agents to drive business outcomes. Rachad Alao, vice president of product engineering at Cohere, joined VentureBeat CEO Matt Marshall for a fireside chat about building agentic systems without surrendering sensitive data, infrastructure control, or the ability to change vendors. Alao, previously leading responsible AI and trust and safety engineering teams at Google and Meta, argued that AI sovereignty means more than downloading an open model or running an application behind a corporate firewall.

When asked how Cohere defines sovereignty, Alao pointed to organizations operating mission-critical systems, including banks, hospitals, and governments. “You want to have very tight control on where the data resides, have tight control on the AI,” he said, adding that AI operations should take place in jurisdictions an organization understands or directly controls. That extends from GPUs and private-cloud infrastructure through governance systems that route requests among models, as well as the connectors, search tools, and agent frameworks acting on enterprise data.

“You want to have control on the entire stack,” Alao said. Marshall challenged one of the central economic arguments for smaller, locally deployed models: Inference prices continue to fall rapidly, potentially weakening the case for optimizing every token. Alao countered that total consumption is climbing even faster as enterprises move from relatively simple chatbots to agents that reason through problems, call tools, search internal systems, and take multiple steps before returning an answer.

“Your token utilization is going exponentially up, because you’re dealing with more and more complex agentic use cases,” he said. Those workflows require “a lot of processing, thinking, tools interaction” to complete their objectives, he added. Alao also drew a contrast between providers that bill customers according to token consumption and Cohere’s approach.

“If your whole way of charging customers is for token utilization, you want to maximize token utilization,” he said. “We do not sell our models and our platform that way.” Instead, Alao said Cohere tries to help enterprises solve their hardest problems privately and securely while reducing unnecessary model usage. His prescription was straightforward: “Use the right model for the task at hand.” Rather than sending every request to the largest available frontier model, enterprises should route work according to the intelligence required and the sensitivity or regulatory burden attached to the task.