August 6, 2025
5 min read
Dillon Lawrence
Learn the difference between true agentic AI and automation, why AI agent washing risks business trust and innovation.
What Is AI Agent Washing And Why Is It A Risk To Businesses?
You’ve heard of greenwashing and AI-washing? Now, a new scam called agent washing is emerging in the AI industry. Analysts at Gartner report that out of thousands of AI products claiming to be "agentic," only about 130 truly meet the criteria. Agentic AI tools are considered the next generation of AI, capable not only of processing information and generating content (like ChatGPT) but also of taking autonomous action. To be truly agentic, AI applications must:- Complete complex tasks
- Engage in long-term, goal-oriented planning
- Operate with minimal human intervention
- Interface with other systems using tools like web browsers or by writing and executing code
- RPA systems automate repetitive tasks by following pre-programmed steps but do not reason, plan, or make decisions.
- Some LLM tools can access external systems via APIs but require explicit instructions rather than independently figuring out how to interact. True agentic AI should be able to autonomously discover how to communicate with new systems, even writing code if necessary. Tools that merely orchestrate multiple AI systems without autonomous long-term planning or decision-making are also often mislabeled as agentic.
- A chatbot may write emails on command, but an agentic AI could identify the best recipients, send emails, monitor responses, and generate personalized follow-ups.
- In e-commerce, a chatbot might search a catalog, but an agentic AI could shop across multiple sites, compare prices, place orders, and make payments autonomously.
- Misleading businesses and consumers about AI capabilities, leading to wasted investments and loss of trust.
- Eroding trust in AI technology itself, which could slow down adoption and innovation.
- Operational risks from overreliance on AI systems that cannot handle critical tasks, potentially causing revenue loss or legal issues.
- Hindering genuine AI innovation by making it harder for real breakthroughs to gain support and funding.
- Build AI literacy within organizations and individually to distinguish between true agentic AI and mere automation.
- Demand transparency and accountability from AI vendors about their products’ real capabilities. By understanding these distinctions, businesses can make informed decisions and support the development of genuinely autonomous AI systems.
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What Is Agent Washing?
Agent washing is the practice of marketing existing automation technologies—such as LLM-powered chatbots or robotic process automation (RPA)—as agentic AI when they lack true autonomous capabilities. For example:Hypothetical Examples
Why Is Agent Washing Dangerous?
Gartner predicts up to 40% of agentic AI projects will fail or be canceled by the end of 2027 due to misunderstandings and inflated expectations. The dangers include:How to Avoid Falling Victim to Agent Washing
Frequently Asked Questions (FAQ)
Understanding Agentic AI
Q: What distinguishes agentic AI from traditional automation or chatbots? A: Agentic AI goes beyond simple automation by possessing the capability for autonomous action, long-term planning, and decision-making with minimal human intervention. Traditional automation follows pre-programmed steps, while chatbots primarily generate content based on prompts. Agentic AI can independently interact with systems and even write code to achieve its goals. Q: Can you provide an example of a truly agentic AI application? A: A truly agentic AI in e-commerce, for instance, could autonomously browse multiple online stores, compare prices, identify the best deals, place an order, and even manage the payment process without explicit step-by-step instructions from a human user. It would continuously monitor and adapt to achieve the goal of purchasing a specific item at the best possible price.Risks and Implications
Q: What are the primary risks associated with agent washing for businesses? A: The main risks include financial loss due to investing in non-agentic solutions, erosion of trust in AI technology, operational failures from systems unable to handle complex tasks autonomously, and a slowdown in genuine AI innovation as hype overshadows real advancements. Q: How might agent washing impact the adoption of AI technology? A: Agent washing can create inflated expectations and subsequent disappointment, leading businesses and consumers to become skeptical of AI capabilities. This can hinder the broader adoption and investment in AI technologies, including genuinely transformative agentic systems.Identifying and Avoiding Agent Washing
Q: What are key indicators that a vendor might be engaging in agent washing? A: Look for claims of "autonomous action" or "self-driving AI" without clear evidence of capabilities like complex task completion, long-term planning, or the ability to interact with novel systems independently. Products that merely orchestrate existing AI models or rely heavily on pre-programmed workflows without emergent reasoning are often mislabeled. Q: How can businesses ensure they are investing in genuine agentic AI solutions? A: Businesses should prioritize vendors who provide clear, verifiable demonstrations of autonomous capabilities, offer transparency about their AI's architecture and decision-making processes, and have a track record of delivering on complex, goal-oriented tasks with minimal human oversight. Building internal AI literacy is also crucial for making informed assessments.Source: Originally published at bernardmarr.com on 5 August 2025.