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“Agent washing” is the practice of marketing existing AI assistants, chatbots or robotic process automation (RPA) as agentic AI without substantial agent capabilities. Gartner forecast in June 2025 that more than 40% of agentic-AI projects would be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. That is a prediction, not a measured failure rate—and it does not mean every product sold as an AI agent is merely a chatbot with a new name.
What is agent washing?
Gartner uses “agent washing” for vendors’ relabeling of existing products as agentic AI without adding substantial agentic capabilities. Its June 25, 2025 press release put it this way: “Many vendors are contributing to the hype by engaging in ‘agent washing’ – the rebranding of existing products, such as AI assistants, robotic process automation (RPA) and chatbots, without substantial agentic capabilities.”
The phrase describes a marketing problem, not a formal certification category. The label alone cannot tell you how much autonomy a particular product has. A vendor might use “agent” for a feature that handles a narrow task with tightly controlled steps, or for a system that can plan and act across several steps. Buyers need to inspect what the product actually does, what it can access, and where people remain in control.
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How many agentic AI projects does Gartner expect to be cancelled?
Gartner forecast in 2025 that more than 40% of agentic-AI projects would be cancelled by the end of 2027. It named three reasons: escalating costs, unclear business value and inadequate risk controls. The figure is a forward-looking forecast; it is not a finding that 40% of projects had already failed, nor a prediction that 40% of all AI projects would be cancelled.
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That warning sits alongside a separate Gartner forecast: 40% of enterprise applications would feature task-specific AI agents by the end of 2026, up from less than 5% in 2025. These figures concern different things. One forecasts how widely task-specific agents may appear within enterprise applications; the other forecasts cancellations of agentic-AI projects. More features carrying an agent label do not, by themselves, show that projects deliver useful results or meet governance requirements.
Are AI agents just chatbots or RPA with a new label?
Not necessarily. A chatbot may respond to prompts without taking action in other systems. A traditional RPA flow generally follows predefined rules and steps. An agentic system, as a practical buying distinction, may select and sequence actions toward a task, use tools or data sources, and adapt within defined limits. Products can combine these approaches, so the categories are not a simple pass-or-fail test.
| Type or label | Typical behavior to check | Useful question |
|---|---|---|
| Chatbot or AI assistant | Responds to a user, often by generating or retrieving information; it may have no permission to change external systems. | Does it only answer, or can it take an action and verify the result? |
| RPA workflow | Executes a defined sequence of steps, usually against known conditions; exceptions may require a person or a separate workflow. | What happens when the input, screen or expected condition changes? |
| Task-specific agent | May choose among permitted actions or tools to complete a bounded task; its autonomy and fallback behavior depend on the implementation. | Which decisions can it make, and which actions require approval? |
This comparison is a way to evaluate behavior, not a universal technical definition. A system that uses an LLM is not automatically an agent, and a tool-using agent need not be unrestricted or fully autonomous. The useful distinction is the system’s actual ability to plan and act, the boundaries around that ability, and whether the result can be checked.
What do Gartner’s adoption figures say—and not say?
In a 2025 survey, Gartner reported that 75% of surveyed IT application leaders were piloting, deploying or had deployed some form of AI agent. In the same survey, 12% strongly agreed that agents would replace applications within two to four years, while 7% strongly agreed that agents would replace workers in that period. These are survey responses about activity and expectations, not independent proof that the agents worked well or that replacement would occur.
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Together, the figures point to experimentation alongside comparatively limited strong expectations of near-term replacement among respondents. They do not establish what proportion of vendors are agent washing: Gartner’s cited release says “many” vendors engage in it, but does not quantify the share.
How can you tell whether an AI agent is real enough for your use case?
Ask the vendor to demonstrate the intended task end to end in a representative environment. Focus on observable behavior and controls rather than the product name.
- Autonomy: Can it plan or select actions across multiple steps, or does it merely answer a question or follow a fixed script? Which decisions are fixed, and which are made dynamically?
- Tools and data: Which applications, APIs, files and records can it read or change? Are those connections available in your required systems and environment?
- Permissions: Can the agent act only under a named user or service identity, and can access be limited to the minimum data and actions needed? Ask how permissions are enforced, not just configured.
- Human approvals: Which actions pause for review? Can approval requirements be set for consequential or irreversible actions?
- Fallbacks and exceptions: What does it do when information is missing, an integration fails, or confidence is low? Can it stop safely and hand the case to a person?
- Audit trail: Can an administrator see the inputs, actions, tool calls, approvals and final outcome for a run? Is the record useful for investigating errors?
- Security and incident response: How is the system tested against unauthorized access, unsafe instructions and unintended actions? Who can disable it, contain an incident and review what happened?
- Measured value: What business outcome is the deployment meant to improve, and how will you measure it against the existing process? Include exceptions and human review in the comparison.
- Operating cost: Ask for the full cost model, including integration, data preparation, model or usage charges, monitoring, maintenance and human oversight. A successful demonstration does not establish that recurring operation is economical.
- Portability: Can workflows, prompts, policies, logs and integrations be exported or reused elsewhere? What would need to be rebuilt if you changed models or vendors?
What should you check before buying an agent platform?
Treat the purchase as a scoped project with a defined process, rather than buying autonomy as an end in itself.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute- Choose a bounded task. Identify the current process, its inputs and exceptions, the systems involved, and the outcome the business needs. Avoid starting with a vague goal such as “automate support.”
- Map the access boundary. List the data and actions the system needs, then define what it must not access or change. Set permission boundaries and approval points before connecting live systems.
- Test representative cases. Include normal work, incomplete information, ambiguous requests, integration failures and attempts to trigger an out-of-scope action. Record when the agent completes, asks for approval, falls back or fails.
- Establish a baseline and a stop condition. Compare results with the existing process using a business measure chosen for the task. Decide in advance what level of error, cost or unsafe behavior pauses the rollout.
- Plan for operation and recovery. Assign owners for monitoring, access changes, incident handling and updates. Confirm that staff can review or reverse actions where appropriate and that logs support investigation.
- Reassess before expanding. A successful result on one task does not prove that the same permissions, controls or economics will work for another. Extend scope only after evaluating the new task and its risks.
Why are projects at risk of cancellation?
Gartner’s stated cancellation drivers translate into three practical tests. First, costs can grow beyond the initial demonstration when integrations, data quality, monitoring and human handling of exceptions are included. Second, a project can lack a clear business case if it has no defined baseline or measurable outcome. Third, inadequate risk controls can make a system too unsafe or difficult to govern for its intended permissions and scope.
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Gartner also forecast in 2026 that 40% of enterprises would demote or decommission autonomous agents by 2027 because of governance gaps. That is another forecast, distinct from the 2025 project-cancellation prediction. It reinforces why evaluation should cover permissions, approvals, auditability and incident response alongside capability and cost.
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