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Intelligent Agents Revolutionize Business: How Two Giants Are Harnessing Agentic Ai For Customer Transformation
The Rise of Intelligent Agents: Lessons from Intuit and Amex on Embracing Agentic AI
Generative AI continues to mature, with enterprises shifting their focus from experimentation to implementation. Intelligent agents are transforming customer experiences, internal workflows, and core business operations. Two companies, Intuit and American Express (Amex), are at the forefront of this shift.
Intelligent agents are not just about answering questions; they’re about executing tasks. At Intuit, 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.
Intuit’s custom generative AI operating system, GenOS, is at its core. GenRuntime, similar to a CPU, 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.
GenOS helps create consistent, trusted experiences across Intuit’s brands—from TurboTax and QuickBooks to Mailchimp and Credit Karma—ensuring robustness, scalability, and extensibility across use cases. The system abstracts away technical complexity, allowing developers to focus on building agentic capabilities without duplicating work.
Across Amex’s brands—from loyalty programs to online services—the company is reshaping its strategy to focus on how intelligent agents can drive internal workflows and power the next generation of customer experiences. 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.
Amex developed an “enablement layer” to support fast experimentation, strong security, and policy enforcement. This enablement layer allows for rapid development without sacrificing oversight. Within this system is Amex’s concept of modular “brains”—a framework in which agents are required to consult with specific “brains” before taking action.
These brains serve as modular governance layers—covering brand values, privacy, security, and legal compliance—that every agent must engage with during decision-making. Each brain represents a domain-specific set of policies, such as brand voice, privacy rules, or legal constraints and functions as a consultable authority.
By routing decisions through this system of constraints, agents remain accountable, aligned with enterprise standards, and worthy of user trust. For example, a dining reservation agent operating through Resy, Amex’s restaurant booking platform, would need to validate that it’s selecting the right restaurant at the right time, matching the user’s intent while adhering to brand and policy guidelines.
Both AI leaders agreed that enabling rapid development at scale demands thoughtful architectural design. Intuit’s creation of GenOS empowers hundreds of developers to build safely and consistently. The platform ensures each team can access shared infrastructure, common safeguards, and model flexibility without duplicating work.
Amex took a similar approach with its enablement layer. Designed around a unified control plane, the layer lets teams rapidly develop AI-driven agents while enforcing centralized policies and guardrails. It ensures consistent implementation of risk and governance frameworks while encouraging speed. Developers can deploy experiments quickly, then evaluate and scale based on feedback and performance, all without compromising brand trust.
The companies stressed the need to move quickly but with intent. “Don’t wait for a bake-off,” Amex’s EVP and CTO Hillary Packer advised. “It’s better to pick a direction, get something into production, and iterate quickly, rather than delaying for the perfect solution that may be outdated by launch time.”
They also emphasized that measurement must be embedded from the very beginning. Intuit’s Chief Data Officer Ashok Srivastava stressed that instrumentation isn’t something to bolt on later—it has to be an integral part of the stack. Tracking cost, latency, accuracy, and user impact is essential for assessing value and maintaining accountability at scale.
“You have to be able to measure it,” said Srivastava. “That’s where GenOS comes in—there’s a built-in capability that lets us instrument AI applications and track both the cost going in and the return coming out.” He reviews this every quarter with his CFO, assessing exactly how much he’s spending and what value he’s getting in return.
As enterprise expectations evolve from simple chatbot functionality to autonomous execution, organizations that treat agentic AI as a first-class discipline—with control planes, observability, and modular governance—will be best positioned to lead the agentic race.