Gartner analysts have highlighted a growing trend where some unethical vendors are participating in what’s known as “agent washing.” Out of thousands of AI products claiming to be agentic, only 130 have actually met the criteria.

Agentic AI is often promoted as the future of artificial intelligence. Unlike traditional systems that merely process and generate content (like ChatGPT), true agents can perform actions autonomously.

For an application to be genuinely agentic, it must handle complex tasks and long-term planning with little or no human involvement. This is achieved by interacting with external tools such as web browsers or being able to write and run code independently.

So, what exactly is agent washing? According to the report, it's when vendors falsely label existing automation technologies — such as LLM-driven chatbots and robotic process automation — as agentic AI, even though they lack the necessary capabilities.

How can one differentiate between a vendor offering authentic agentic AI and one engaging in misleading labeling? And why is this kind of misrepresentation particularly concerning?

Agentic Or Just Agent Washing?

Without a clear understanding of what makes AI truly agentic, it's easy to be misled by incorrect labels.

Sometimes the confusion comes down to terminology. The word “agent” has broad usage, even though it has a very specific meaning in modern AI discussions.

Take for example so-called AI customer service agents. These are typically just chatbots that can respond to queries or connect users to humans for more complex issues — they don’t actually take any independent action.

The Gartner report also points out that robotic process automation (RPA), which relies on pre-programmed sequences to complete repetitive tasks, is being inaccurately branded as agentic AI.

While RPA systems do perform actions like updating ledgers or inventory systems automatically, they don't possess the ability to reason, strategize, or make decisions.

Some large language models can interact with external platforms via APIs, but only if given direct instructions. True agentic systems should be able to figure out how to use unfamiliar APIs without guidance. If they can't communicate naturally, they should generate and execute code to bridge the gap.

Even platforms that combine multiple AI tools — like marketing automation or workflow systems — are stretching the definition unless they can independently coordinate these tools over extended periods for strategic planning.

To illustrate: a regular chatbot might compose emails upon request, while an agentic system would not only draft them, but also determine the best recipients, send them, track responses, and craft personalized follow-ups.

In online shopping, a standard chatbot could help you find items from a single catalog. But a real agent would search across various websites, compare prices, and place the order on your behalf.

Without recognizing these key differences, it's easy to mistake a powerful generative AI for a fully autonomous agent.

Why This Trend Poses Serious Risks

Gartner estimates that as many as 40% of agentic AI projects will either fail or be abandoned by 2027. This suggests that inflated claims and misunderstandings are becoming a major issue for organizations.

One of the most immediate dangers is that businesses and consumers may be deceived about the actual performance of AI solutions. When expectations aren’t met after significant investment, it could damage trust between companies and AI providers.

This mistrust could extend beyond vendors to the broader field of artificial intelligence itself. For those who believe in AI’s potential to drive meaningful progress, this erosion of confidence could hinder adoption and development.

Beyond reputational risks, misunderstanding AI's abilities can lead to operational failures. Overestimating the capacity of these systems to manage critical tasks — from handling complaints to detecting cyberattacks — could result in financial loss, missed opportunities, or regulatory breaches.

In the long term, agent washing threatens legitimate innovation in AI. Startups and researchers developing real agentic technologies may struggle to secure funding or support amid a flood of misleading claims.

Gartner warns that agent washing doesn't just result in failed initiatives — it weakens the entire AI ecosystem’s credibility and slows the delivery of impactful tools.

Ultimately, the solution lies in improving AI literacy at both individual and organizational levels. Understanding the distinctions between genuine agentic behavior and basic automation allows us to better evaluate which systems can truly adapt and plan over time.

Vendors must also be held accountable for clearly communicating the strengths and limitations of their offerings.

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