How AI agents and agentic AI differ from each other

With agentic AI in its infancy and organizations rushing to adopt AI agents, there seems to be confusion about the difference between the two technologies.

Many people have used the terms “agentic AI” and “AI agents” interchangeably, but experts say there’s growing understanding that the two are separate, but related, tools. CIOs should understand the difference to ensure they’re using the right tool for the job, experts say.

AI practitioners are beginning to define the two technologies this way: AI agents are tools tasked with a specific function within an organization’s IT systems, with predictable outcomes as the goal. Agents have a narrow scope and generally have a limited ability to learn new information.

Meanwhile, the still nascent agentic AI is an umbrella technology that can use agents and other AI tools to create fully autonomous systems that can set their own goals, learn over time, and reason across tasks, they say.

Some companies are starting to deploy early-stage prototypes of agentic AI, but a truly autonomous system has long-term persistent memory and other capabilities that aren’t available yet, says Numa Dhamani, head of machine learning at mobile security provider iVerify.

“Agentic AIs have the ability to set or reprioritize their goals, and they’re able to kind of dynamically reason through different kinds of domains,” she says. “They can do things like self-reflection or improvement loops, and they’re also starting to make decisions about which tasks to perform, and how they would sequence those tasks.”

CIOs can think of agents as individual players or employees while agentic AI is the larger team, says Jim Olsen, CTO at software governance provider ModelOp. “Each member of the team brings both abilities, or tools, and expertise, or training, to an overall task, while agentic AI is the whole team working together to solve the problem,” he adds.

Louis Gutierrez, director of AI at email marketing platform provider Constant Contact, also uses the player and team metaphors.

“Agentic AI is more like an orchestration layer — a system that supervises and coordinates multiple agents to tackle broader objectives,” he adds. “If agents are individual players, an agentic system is the coach, team, and playbook all working together.”

Why should CIOs care? Vendor obfuscation

While the difference between the two AI technologies may sound like a semantic exercise, the distinction is important for CIOs because both are getting a huge amount of hype right now, iVerify’s Dhamani says. In some cases, vendors are selling technology they describe as agents or agentic but really isn’t.

“You’re probably just overpaying for glorified chatbots that are dressed up like an agent,” she says.

CIOs should be wary of vendors trying to sell them agentic AI, she adds, because the technology is still in its early stages. After model context protocol (MCP) and other agent-connection protocols were recently released, AI developers are beginning to take steps toward fully autonomous agentic AI, but they aren’t yet reaching the shared memory or tool orchestration capabilities needed, she says.

Some vendors may overpromise, underdeliver, and sell systems that act unpredictably, Dhamani adds. “I see a lot of people marketing agentic AI, when in reality, it’s really just a chatbot with some RAG,” she says. “It’s a chatbot just retrieving some documents, or calling a calculator, and that’s not an agentic experience.”

If an AI vendor can’t explain how its technology works, that’s a red flag, says Constant Contact’s Gutierrez. “It’s worth clarifying whether you’re buying a true agentic system or just a workflow agent dressed up in buzzwords,” he adds. “The biggest danger in this space is obfuscation — it’s not always intentional, but it is common to oversell what the technology actually does.”

Without a full understanding of how agents and agentic AI work, CIOs and other IT leaders could also fail to realize the risks involved and the oversight needed, Dhamani says.

“You need to monitor them, and you need to audit them,” she adds. “If you are now starting to do things like tool calling and initiating actions, you’re starting to introduce a lot of coordination complexity, and you now have an increased blast radius. What can go wrong will probably go wrong.”

Do you really want autonomous bots?

One of the first questions CIOs should ask themselves as agentic AI emerges is whether they actually want autonomous AIs running through their IT systems, adds ModelOp’s Olsen.

“True agentic AI relies on giving the solution a fair degree of autonomy to figure out how it should best accomplish the task,” he says. “Final review is absolutely recommended, as things can go off the rails. Do you really need that full level of autonomy to accomplish your task, given the high risks it could present to your business?”

Connecting agents to one another using MCP or other protocols also creates risks of data exposure, Olsen says.

“If you truly want to do agent to agent, what data can basically leak through that pathway?” he asks. “You have this tool that can only access this customer database, but has access to Social Security numbers, but then the agent sends it over to the one that has access to Slack and starts posting those numbers on a public Slack.”

Olsen sees both technologies evolving quickly. Agents, now powered by large-language model (LLM) AIs, will instead rely on small-language models (SLMs) trained to deal with the agent’s assigned task, he says. Agents will become smarter and more reliable.

“We will start having what I believe will be truly expert agents, where you’ll use an SLM, highly trained on a specific thing, just like you would get a software programmer and an agile expert, and a product manager together,” he says. “You’ll start to build up specialized team members, or agents, that will then be very good at performing those specific tasks.”

Even with a promising future, CIOs and other IT leaders considering either AI variant should do their homework before diving in, Dhamani recommends. They need to understand how the AI will be supervised, and they need to make sure their data is cleaned up and ready for use by agents or agentic AI, she says.

She advises organizations to start small. “I would start with a very constrained use case that is low risk,” she says. “Keep the agent in a read-only or suggest-only mode to start with, and then just gradually increase autonomy after it meets all your performance thresholds.”