Developer skills will also change of time as their workflows become more agentic. “It’s less important that our developers are writing actual lines of code and more important that they understand the context and the inputs and the outputs that are being required in the change,” he says.

Gusto CIO and CISO Mike Wittig says the company has already created AI agent manager roles where people are responsible for the performance, roadmap, and metrics of AI agents.

“This is a hybrid of product and engineering roles where the agent manager needs to understand their stakeholders and meet regularly with them to review health metrics and roadmap items for the agent, as well as contribute to the backlog by building new agent functionality,” he says. “This also ensures human accountability for agentic activity, which is a core tenet of our strategy as we continue to scale with AI.”

Palo Alto’s Rajavel also sees roles, such as in software development, evolving over time. While engineers still need to make modifications to AI-generated code, she says, “[AI] does a significant lift. So I have to reimagine the role of some of our software engineers.” Now they spend more time on design thinking and building security into the code, as well as scalability and performance.

Also, pursuing agentic AI is changing how IT and the business collaborate, she adds.

“Our [product managers] are not anymore just sitting with the business talking about the requirements. They are actually sitting with the business building out a prototype version before even getting out of the conversation,” Rajavel says. “That means you need the product manager to be more tech savvy and more comfortable with using AI tools to go from a business problem to a conceptual solution.”

Similarly, she adds, software engineers should be more comfortable “cutting across rather than [making] a narrow slither to more depth and breadth.”

Deciding where agentic is a logical fit

When deciding where agentic makes sense, Rajavel looks at whether a workload requires reasoning, some level of understanding, and continuous learning, she says.

Her roadmap for the next year is to determine which AI technologies have the highest ROI for disruption. Often, it takes 12 to 18 months to gain the full benefits, she notes. That was the case with Panda AI — it took about 12 months to go from 12% automation to 16% automation, and today, it stands at 83%.

Further, IT is constantly revising what its business priorities are, which makes targeting the right workflows for an agentic overhaul all the more important.

“Make sure your use cases are the right fit for it,” she advises, adding that here data volume and integrity is important. Often, people hold onto a lot of “tribal knowledge,” she says, and you have to figure out how to give the agents that knowledge, “otherwise your agents are not going to be effective.”

She also advises not underestimating the importance of “keeping the agents continuously on track.” That requires “having clarity of ownership and making sure … you have a person who’s accountable to make sure this is actually behaving as intended and having the right guardrails and right evaluations,” she adds.

While all the IT leaders say that AI and agentic are helping their organizations move faster and be more efficient, Gusto’s Tria says that creates a perspective that security is being sacrificed.

“Moving faster does not mean you are dropping rigor. Moving faster does not mean that you are less reliable or less secure,” he says. “We have a lot of confidence internally that the very same agentic capabilities that make us go faster also increase the reliability of our systems. … It’s actually a win-win.”