Rethinking and Realigning IT for the Agentic AI Era

Even as a seasoned IT leader, Richard Mackey’s jaw drops in amazement over what AI and AI agents can help him do — especially when it comes to the tasks he dislikes the most. “It’s like my long-lost friend,” says Mackey , CTO of healthcare company CCS. For example, while Mackey has become adept at creating PowerPoint presentations, he appreciates how fast an agent can make one for him. Recently, Mackey delivered a presentation to CCS’s CEO and others that an agent put together, which he admits was pretty good. “This would have taken me four hours … and the basic part of it was done in 15 minutes, which is crazy.” Having an agent do this shifts the work for humans, he says. “I like this approach because it creates the starting point.” Mackey adds that he used a watermark to attribute the presentation to AI and modified about 20% of it, but “those are the kinds of things that I find all the time are helping me go faster.” It’s that kind of ringing endorsement that is helping fuel the rise of AI agents, prompting IT leaders to fundamentally rethink how IT operates as they move beyond chatbots and copilots to autonomous systems that make decisions, execute workflows, and interact with multiple enterprise systems. That reset includes how they should organize IT teams, leverage data, re-engineer their technical stacks, and interact with business partners to make the most of agentic AI. The big picture Three-quarters of leaders at enterprise organizations are adopting agentic AI, but only a small minority have implemented meaningful production applications, according to Forrester’s State of Agentic AI 2026 report. Even companies at the leading edge have not yet realized the expected value promised by agentic, the report states. “In 2026, we see agentic technology rapidly changing software markets, even as enterprises slog through the morass of challenges between promise and payoff,” the report notes. “The core question is, will the engine of agentic pull the enterprise payload?” While that remains to be seen, agentic AI requires organizations to move beyond individual models toward governing the whole system in which models operate, observes Rashmi Shetty , vice president of enterprise AI at Capital One. “That means governing the data and context moving across agents, their identities and permissions, the tools they can access, the actions they are permitted to take, and the points where a human needs to step in.” Rashmi Shetty, VP of enterprise AI, Capital One Capitol One Amid the exuberance surrounding agentic in the enterprise, continuous evaluation and observability remain important, Shetty stresses. “Teams need to understand how agents are behaving in production, which tools they are invoking, whether agentic systems are achieving their intended goals, how the system performs end to end, and where latency or failure occurs.” The organizations best positioned to scale agentic AI “will build these capabilities into enterprise platforms from the outset, creating a governed path from rapid experimentation to safe, reliable production deployment,” Shetty says. Reimagining how work is done Understanding the difference between AI and agents is critical, says Meerah Rajavel , CIO of Palo Alto Networks, noting that AIs involved in automation are sometimes misconstrued as agents. As opposed to generic AI automation, agents perform specific work and must be capable of reasoning, continuously learning, and updating its memory, Rajavel says. Agentic workflows take that concept a step further, clustering agents to work together to achieve a task. “An agent is an atomic task level, and an agentic workflow is achieving a business outcome in a complex world,” she explains. “Getting to a true agent does take a complete reimagination of how you do the work,” she adds. At Palo Alto, all employees have access to Panda AI, an internal agent built with AI workflows more than a year ago to handle IT, travel, expenses, and HR issues. Panda AI has automated 82% of tickets and reduced IT operational costs by almost 70%, Rajavel says. Meerah Rajavel, CIO, Palo Alto Networks Palo Alto Networks Palo Alto responds to more than 900 RFPs annually, and IT has built another AI agent that automates the first draft of complex RFPs, which is then validated and refined by a team of consultants. “It used to take six to eight weeks to get a good RFP done,” Rajavel says. “Today, it’s a matter of hours.” The company is in the early stages of building more agents, but it’s a process, she says. “Before you build an agent or agentic workflow, you really have to go and spend a lot of time looking at the data” and determine what data is and is not available, Rajavel says. “You need to really do a deep rip out of your current state, and then you have to completely forget [how work is done] today and go to a clean sheet and say, ‘What is our first principle thinking in an agentic world? Should we then bridge it?’ That’s where the trick comes.” Always test your infrastructure Mike Tria , CTO of online payroll provider Gusto, says his company is moving toward agentic capabilities that will be autonomous and proactive. To get there requires investing in an automated testing infrastructure, he says. When building agentic capabilities, the goal is to ensure that the systems are doing what they’re designed to do, Tria says. It’s no longer just about continuous integration — the second layer is adding the ability for the agent to conduct evaluations. Mike Tria, CTO, Gusto Gusto “Having a really good test infrastructure will tell you when you have outdated data,” Tria explains. “It’s expensive for companies to do vast exploration. Agents can be pretty smart about knowing when something is outdated. Lean on that first.” Companies are investing in knowledge layers and ontology systems, and IT should build something that sits above the data that helps describe the business and how its systems work, he says. “Give your agent access to that. You should fix the data, but you want to make sure you fix the right stuff.” Changing the nature of work CCS has just over 1,000 employees and serves hundreds of thousands of clients. Mackey says that when IT began looking at how agentic could help its business, there were many potential areas, including corporate, sales and marketing, and the contact center. Like many organizations, CCS opted to begin with the latter because officials saw a good opportunity to increase call resolution over time. So the IT team introduced an agent called CC at the start of 2026, and now over 30% of calls are being resolved autonomously. What officials didn’t anticipate was that as agents handled more calls, the ones that reached humans became more complex. It wasn’t that the agents were failing, but that the human conversations required more judgment and problem-solving, changing the nature of the work. “What we like to say is we’re not in the business of trying to replace humans with machines,” Mackey notes. “We’re in the business of augmenting our human capabilities with these tools.” Richard Mackey, CTO, CCS CCS The goal is to help patients and teams automate where it makes sense, he says. At the same time, Mackey adds, they are not trying to replace clinical judgment. “We’re not trying to have machines make decisions around or judgments or evaluations of any kind of a clinical nature, but we are trying to automate tasks that may be more mundane and more mechanical,” he says. Employees are now addressing “more of the exceptions” that come into the contact center, and those exceptions take longer sometimes to resolve, he says. When CC was first rolled out, IT didn’t anticipate that there was, and will likely always be, a significant number of people who want to talk to a human no matter what, Mackey notes. However, “the next wave of investment” will be to build features that enable the agents to offer up what they can help with when a caller asks to speak to a human. The agent will then as