What Is AI Agent Washing, And Why Is It Everywhere?
By Aytekin Tank,
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Aytekin Tank is the founder and CEO of Jotform.
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Aug 20, 2025, 02:51am EDTAug 20, 2025, 08:44am EDT
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AI agents, in case you haven’t noticed, are everywhere.
Every week, I see new headlines about “autonomous AI,” “self-directed agents,” and “intelligent coworkers.” As someone who’s spent years building automation tools, I understand the excitement—some of those headlines are even written by me.
The promise of AI agents—tools that can plan, reason, and act independently—is genuinely game-changing. But not everything labeled an “agent” actually lives up to the name.
More and more, I’m noticing a trend that feels uncomfortably familiar from past tech booms: companies rushing to call their simple chatbots or task automations “agents,” when in reality, they’re little more than rebranded scripts. This practice is called “agent washing,” and while it may help companies ride the current hype cycle, it comes at a cost—it confuses users, disappoints customers, and gums up the process of adopting tools that are genuinely transformative.
Here’s how to recognize agent washing and why it matters.
What Is Agent Washing?A new report from Gartner throws cold water on the agent craze, estimating that only about 130 of the thousands of existing agentic AI vendors are legit.
“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” said Anushree Verma, a Senior Director Analyst at Gartner. “This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production.”
Some companies are compounding the problem through agent washing, which involves rebranding existing products like AI assistants and chatbots without “substantial agentic capabilities.”
When that line is blurred—whether to attract attention, funding, or users—it has the effect of creating confusion and eroding trust. Users expect the intelligence and autonomy that the word “agent” implies, and when that expectation isn’t met, it reflects poorly on the product and the brand. Worse still, it can stall meaningful progress by flooding the market with inflated claims and underwhelming experiences.
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The Promise Of Real Agents Vs. HypeWith so much noise in the market, it can be difficult to tell the difference between a true AI agent and a dressed-up LLM workflow. But with a sharp eye, it’s possible. As Yoav Shoham, Stanford professor emeritus and AI21 Labs cofounder, put it in MIT Tech Review, “We don’t necessarily need a rigid standard, but we do need clearer expectations about what these systems are supposed to do, how autonomously they operate, and how reliably they perform.”
Asking just a few key questions can go a long way toward cutting through the hype.
The most important thing to check for is whether the tool can make autonomous decisions without constant human input. A real agent doesn’t just wait for you to tell it what to do. It assesses the situation, weighs options, and chooses a course of action based on context. If it simply reacts to pre-set triggers, it’s likely just automation. In banking, for example, agents should be able to not only identify suspicious activity; they should take the appropriate safeguarding actions without being prompted.
Additionally, true agents should demonstrate that they’re learning and improving over time. They adapt to user behavior, refine their strategies, and adjust based on outcomes. If the system performs the same way no matter how many times it runs, that’s your sign you’ve been snared by a marketing gimmick.
Lastly, Shoham makes the important point that even the smartest agent isn’t useful in a vacuum. “Without a shared vocabulary or context, coordination becomes brittle,” he writes. “Solving it at scale is far from trivial.” Of course, agents can be taught to negotiate and collaborate, but it won’t happen automatically. “The potential here is real,” he argues. “But we need to match the ambition with thoughtful design, clear definitions, and realistic expectations.”
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Final ThoughtsAgent washing may seem like a harmless bit of marketing. But in reality, it undermines the entire ecosystem by fostering skepticism, slowing adoption, and turning enthusiasm into disappointment.
As sobering as Gartner’s findings are, there is also reason for optimism. The firm predicts that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024. Beyond that, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.
If we want to build a future powered by truly intelligent tools, we have to start with clarity. That means resisting the urge to chase buzzwords and instead focus on solving real problems with real capabilities.
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