What is AI Agent Cleaning and why is it a risk for businesses?

Have you heard of greenwashing and AI washing? Well, now it seems like the hype guru and bandwagon jumpers with the technology to sell have come up with a new (and perhaps unexpected and inevitable) scam.

Gartner analysts say that uncruel vendors are increasingly engaged in “agent washing,” and of the “thousands” of the agent AI products tested, only 130 people were really meeting the bill.

Agents are widely advertised as the “next generation” of AI tools. In addition to processing information and generating content (for example, ChatGPT), you can also perform actions.

To be a true agent, the application must be able to complete complex tasks and long-term goal-oriented planning with minimal human intervention. This does this by interfacing with other systems using tools, such as web browsers and the ability to write and execute code.

So, what is the scam? According to the report, agent washing involves passing on existing automation technology if they are actually lacking such as chatbots with LLM or automating robotic processes.

So, how do you tell us the difference between AI vendors selling true agent products and those engaged in agent washing? And why is this type of behavior more dangerous than it looks at first?

Agent or agent washing?

Without understanding the difference between agent and regular non-all AI, it's easy to be victimized to mislead.

Sometimes it may be simply a matter of semantics and may not be misleading. The word “agent” is used in many contexts despite its precise meaning in today's AI-Speak.

For example, AI customer service agents are often chatbots that do not have the ability to generate advice or take action beyond connecting users to humans to deal with more complex issues.

Gartner's report suggests that robotic process automation (RPA) is misunderstood by vendors as agent AI, including programming machines to complete tasks by performing a series of pre-determined steps.

The RPA system performs actions (for example, automatically enter sales transactions into the ledger and updates the inventory system). However, it does not meet AI agents standards. Because they have not reasoned, planned or made decisions.

Also, some LLM tools can access and control external systems via APIs, including giving accurate instructions on how to do so. A true agent solution should be able to solve it on its own, even if you've never encountered a particular API before. And, as perhaps so, if you find out you can't communicate with an external system using natural language features, you can write and execute computer code.

To coordinate and attract multiple AI systems, such as marketing automation platforms and workflow automation tools, tools that claim to be agents are stretching the terminology unless they can autonomously coordinate the use of these tools for long-term planning and decision-making.

A few more hypothetical examples: AI chatbot-based systems can write emails to commands, while agent systems may write emails, identify the best recipients for marketing purposes, send emails, monitor responses, and generate follow-up emails tailored to individual responders.

Also, in e-commerce, chatbots may be great for searching for a simple catalog and finding products that meet your requirements. However, agents can shop on multiple sites, compare prices to find the best deal, and ultimately place an order and pay on your behalf.

Without realizing this fundamental difference, it's easy to be impressed with generative AI chatbots that appear to be performing tasks in an agent's way, but are actually not as capable as they seem.

Why is this dangerous?

Gartner predicts that up to 40% of agent projects will fail or cancel by the end of 2027. This means that the possibility of misunderstandings, misunderstandings and excessive predictions is an imminent threat for many businesses.

The most obvious danger is that businesses and the public can be misled about the true capabilities of AI tools and apps. If you find yourself not getting the expected results after making an investment, it could lead to a collapse in trust between the company and the AI ​​industry.

Potentially, this could also affect trust in the concept of AI itself. And this will be a disaster from our perspective, which believes that when used properly, it will create major positive change.

Beyond risks to trust and reputation, misconceptions of the capabilities and limitations of AI systems can lead to serious operational risks. Overconfidence in your ability to deal with critical interventions, from customer complaints to cyber threats, can lead to loss of revenue, business opportunities, and even legal violations.

In the long run, this practice threatens real AI innovation by tackling real agent breakthroughs and making it more difficult for developers and startups to gain traction, support and funding.

Gartner believes that agent washing not only fails AI projects, but also undermines efforts by the entire AI community to provide truly useful products.

Of course, the key to avoiding victims of this trend is building AI literacy as an individual and instilling it throughout our organization.

This gives insights to distinguish between actual agent behavior and mere automation, and helps to plan for long-term and identify systems that can adapt to changing circumstances.

And the vendor itself should be kept at the highest standards of transparency and accountability when it comes to honesty about the strengths and weaknesses of the product.