Agentic AI is all hype for now, says Gartner

Alfonso Maruccia

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The big picture: Everyone in the tech industry is talking about agentic AI. This supposed next-generation evolution of large language model technology is expected to revolutionize everything, but analysts aren't convinced that future will become a reality. It's much easier to talk about and hype up AI "agents" than it is to actually build one.

Gartner has predicted that over 40 percent of so-called agentic AI projects will be canceled by the end of 2027. The technology is too expensive, has unclear business value, and is often poorly suited to real-world use cases, frequently hindering organizations rather than helping them. And that's just the beginning.

Agentic AI is supposed to represent a major leap forward from today's chatbots and AI services, enabling "intelligent" bots that can make autonomous decisions aligned with company goals. These agents are fueling a surge in AI spending, and tech companies are capitalizing on the hype by raising prices for their customers. In theory, AI agents should be able to configure Windows, browse the web, or even write code on a user's behalf.

But none of these so-called revolutions are truly happening, Gartner warns. Most agentic AI projects remain early-stage experiments or proof-of-concept demos driven more by hype than by practical results. These systems are often misused by organizations that underestimate their cost and complexity, which ultimately prevents many projects from ever reaching production.

Also see: AI Agents Explained

Gartner polled 3,412 webinar participants in January 2025, asking how their organizations were approaching the agentic AI revolution. Just 19 percent reported making significant investments in AI agents, while 42 percent were taking a more conservative approach. A small minority (eight percent) had made no investments at all, and 31 percent were either unsure or adopting a wait-and-see strategy.

The ongoing hype surrounding agentic AI is also being inflated by what Gartner calls "agent washing." Many companies are simply rebranding existing technologies – such as chatbots, robotic process automation, and virtual assistants – as cutting-edge agentic AI. In reality, only around 130 out of thousands of so-called "AI agents" assessed by Gartner exhibited genuine agentic capabilities.

"Most agentic AI propositions lack significant value or return on investment, as current models don't have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time," Gartner's Senior Director Analyst Anushree Verma stated.

Despite the inflated expectations and questionable implementations, agentic AI could still offer tangible value if organizations are able to deploy and manage it effectively. According to Gartner's forecast, AI bots will be responsible for at least 15 percent of day-to-day decision-making by 2028, up from virtually zero in 2024. Additionally, a third of enterprise software tools are expected to incorporate agentic AI capabilities by 2028, compared to just one percent in 2024.

While AI hasn't exactly transformed call centers as promised, its next chapter – with agentic AI – might have a brighter future.

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I just tried Rufus - Amazon's agentic AI. Asked it to show me various things. It seemed almost as awful as Amazon search which I didn't think was possible. In the end I told it that it was a waste of time and it told me to seek medical attention - rude!
 
So basically we’re in the part of the hype cycle where every chatbot with a slightly better to-do list is calling itself an agent. Can’t wait for Excel macros to get rebranded as AI 3.0.
 
One of it's most promising use cases right now is enabling non-technical users to ask questions about business data and metadata in an interactive way. But that requires the data to be stored in a centralized and accessible way (lots of companies are learning how badly their data management actually is - even data companies are realizing how much work they need to put into good data and AI governance), someone knowledge to set up the data connections, application, agents, prompting, and/or security (more or less in each category depending on which tech stack they are using), and then it requires popularizing and promoting said application with the business users. So even in this case, it's a lot of work.

There are other agentic use cases like automated troubleshooting and customer (external or internal) support, but they're just as difficult.

Agentic AI is easy to get going in demo settings - like basic chatbots before it. Putting it in production? Fixing mistakes (can you even catch the mistakes)? That's a whole can of worms that makes it tough. LLMOps as some have coined it is MLOps on steroids, often because of the expectations being a bit too high.

Even when all goes swimmingly, the question remains: was it worth it? Often there are productivity gains for a cost, which makes it a soft, squishy thing to evaluate. It might go something like this: "My company now spends 10k/month on supporting this agentic system, but it's saved a few hours per week of employees' time so that they can focus on this other thing. Thing is, I was already paying my employees, so is this extra 10k/month on the balance sheet worth it?"

It's more cut and dry when the CEOs get to use AI to justify layoffs, but so many of them have realized after the fact that the AI wasn't actually ready to replace employees, and now their shortsighted greedy-minded-optimization (borrowing this choice of words from the greedy programming heuristic: choosing the shortest, immediate best path instead of optimizing for the long term, leading to potentially bad outcomes) is bare for all to see.
 
Agentic AI isn't hype, it's just not quite there yet.

I still remember when the internet first came out in the 1990s and comedians were joking its all hype cause who the hell cares about pictures of people with cats and dogs and babies, which is all the internet was in the beginning; just digital pictures. Oh how times have changed.
 
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