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As generative AI matures, enterprises are shifting from experimentation to implementation—moving beyond chatbots and copilots into the realm of intelligent, autonomous agents. In a conversation with VentureBeat’s Matt Marshall, Ashok Srivastava, SVP and Chief Data Officer at Intuit, and Hillary Packer, EVP and CTO at American Express at VB Transform, detailed how their companies are embracing agentic AI to transform customer experiences, internal workflows and core business operations.
From models to missions: the rise of intelligent agents
At Intuit, agents aren’t just about answering questions—they’re about executing tasks. In TurboTax, for instance, agents help customers complete their taxes 12% faster, with nearly half finishing in under an hour. These intelligent systems draw data from multiple streams—including real-time and batch data—via Intuit’s internal bus and persistent services. Once processed, the agent analyzes the information to make a decision and take action.
“This is the way we’re thinking about agents in the financial domain,” said Srivastava. “We’re trying to make sure that as we build, they’re robust, scalable and actually anchored in reality. The agentic experiences we’re building are designed to get work done for the customer, with their permission. That’s key to building trust.”
These capabilities are made possible by GenOS, Intuit’s custom generative AI operating system. At its heart is GenRuntime, which Srivastava likens to a CPU: it receives the data, reasons over it, and determines an action that’s then executed for the end user. The OS was designed to abstract away technical complexity, so developers don’t need to reinvent risk safeguards or security layers every time they build an agent.
Across Intuit’s brands—from TurboTax and QuickBooks to Mailchimp and Credit Karma—GenOS helps create consistent, trusted experiences and ensure robustness, scalability and extensibility across use cases.
Building the agentic stack at Amex: trust, control,and experimentation
For Packer and her team at Amex, the move into agentic AI builds on more than 15 years of experience with traditional AI and a mature, battle-tested big data infrastructure. As GenAI capabilities accelerate, Amex is reshaping its strategy to focus on how intelligent agents can drive internal workflows and power the next generation of customer experiences. For example, the company is focused on developing internal agents that boost employee productivity, like the APR agent that reviews software pull requests and advises engineers on whether code is ready to merge. This project reflects Amex’s broader approach: start with internal use cases, move quickly, and use early wins to refine the underlying infrastructure, tools, and governance standards.
To support fast experimentation, strong security, and policy enforcement, Amex developed an “enablement layer” that allows for rapid development without sacrificing oversight. “And so now as we think about agentic, we’ve got a nice control plane to plug in these additional, additional guardrails that we really do need to have in place,” said Packer.
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