The Founder's Honest Take: Most 'Agentic AI' Products Are Just Fancy Automation With Better Marketing

I'm going to say something that will irritate a few vendors in my LinkedIn feed: most of what is being sold as 'agentic AI' right now is not agentic AI. It is automation with a new coat of paint, a friendly chat interface, and a press release that uses the word 'agent' seventeen times.

I'm not saying this to be contrarian. I'm saying it because the confusion is genuinely hurting buyers, crowding out the builders doing real work, and setting up a wave of failed implementations that will give the whole category a black eye it doesn't deserve.

The Numbers Are Already Telling the Story

Gartner looked at the vendor landscape in 2026 and found that roughly 130 out of thousands of companies claiming agentic AI capabilities actually deliver something that qualifies. Thousands of vendors, 130 that pass the bar. That is not a rounding error. That is a signal.

They also predict that over 40% of agentic AI projects will be canceled before the end of 2027. Not paused. Canceled. Buyers will write off the investment, reassign the team, and move on. The reason will not be that genuine agentic AI failed them. The reason will be that they bought something that was not what it claimed to be, watched it break the moment real complexity arrived, and concluded the whole idea was hype.

That is the specific damage I want to talk about. The washing of the label does not just mislead buyers. It poisons the well for the real thing.

What 'Agent Washing' Actually Means

Gartner coined the term explicitly: agent washing. The practice of rebranding existing products, RPA tools, chatbots, AI assistants, workflow automation platforms, as agentic without adding anything that would actually justify the label. The rebrand is often cosmetic. A new name, a new landing page, an LLM handling the input and output so the whole thing feels more conversational.

An Expereo analyst described it well: agent washing was 'an inevitable consequence of hype outpacing operational readiness.' They noted the same pattern from earlier automation waves, where the market rewarded the label rather than the substance. Vendors learned that lesson fast. If the label sells, you use the label. Whether the product earns it is a secondary concern.

I watched this happen with 'digital transformation.' I watched it happen with 'AI-powered.' Now it is happening with 'agentic.' The cycle is not new. What is new is how quickly it moved.

What Genuine Agentic AI Actually Does

Here is the distinction that matters. A genuine agentic AI system can set its own subgoals in pursuit of a higher-level objective. It takes multi-step actions, sequences them based on what it learns along the way, handles exceptions it was not explicitly programmed for, and adapts. It does not just follow a script. It reasons about what to do next when circumstances change.

Most products on the market today that carry the 'agentic' label are if-then rule engines with a better UI. They have an LLM bolted on to handle natural language in and out, which makes them feel intelligent, but the underlying decision logic is still a flowchart someone built by hand. Swap 'trigger this action when condition X is met' for 'ask the LLM to interpret what the user said, then trigger the action,' and you have not built an agent. You have built a chatbot with structured outputs.

RPA rebranded as agentic is still RPA. The moment it hits an edge case outside its defined paths, it fails or escalates to a human. A chatbot with tool-calling is not an agent in any meaningful sense. It is a very capable question-answering interface that can also take actions, which is useful, but calling it an agent is like calling a calculator a mathematician.

The practical test is simple: what does the system do when it encounters something it was not designed for? A rule engine stops, errors out, or calls a human. A genuine agent reasons about the situation, generates a plan, and tries to solve it. That is the gap. It is not a small gap.

The Label Is Poisoned, and That Is the Real Problem

Here is my actual thesis. The word 'agentic' has been poisoned. Not ruined permanently, but the signal-to-noise ratio on the label is now essentially zero. If a vendor tells you their product is agentic, that information is nearly worthless as a buying signal. You have to go deeper.

This creates real cost for the founders actually building the genuine thing. They are trying to communicate something specific and important about their architecture, about the fact that their system does reason, does adapt, does handle novel situations. And they are doing it with a vocabulary that has been stripped of meaning by three years of marketing inflation.

The founders who will win this category are the ones who have built genuine autonomous reasoning into their products, not just used the word. They are building systems that degrade gracefully under uncertainty instead of collapsing. They are doing the hard work of handling exceptions at the model level instead of routing everything back to a human or a hardcoded fallback. That work is hard. It is also real. It is the thing worth buying.

The buyers who will avoid the 40% project cancellation rate are the ones who go into evaluations with a clear demand for specifics. Not demos, not case studies with convenient edge cases removed, but a direct look at how the system behaves when things go sideways.

One Question Before You Sign

If you are evaluating an 'agentic AI' vendor right now, whether you are buying for your company or building on top of their platform, ask this before you sign anything:

What happens when your system encounters a situation it was not trained for or explicitly programmed to handle?

Listen carefully to how they answer. A vendor with a genuine agent will be able to describe, concretely, how the system reasons through novel situations, what its fallback behavior looks like, and where the limits are. They will be specific because they have actually thought about it.

A vendor selling automation with a new label will pivot immediately to a demo, reference a use case where the system worked perfectly, or describe their 'human-in-the-loop escalation path,' which is a polite way of saying the system cannot handle it and passes the problem to a person.

That answer tells you everything. The real category is worth investing in. The fake version will cost you more than the subscription fee.