For those who read my daily ramblings, you already know that “autonomy” is one of those dirty AI words that has been completely corrupted. Similar to how “sovereignty” has been overused in the wrong context more recently. Engineering, or more precisely, marketing, has a long history of hijacking words to help sell products that don’t actually do what they claim they do.

It’s a large hill to climb. But it’s a soapbox I will continue to stand on.

Here’s another perspective on why the systems we’re building aren’t autonomous. Because the word matters. And using it wrong isn’t just imprecise. It’s the source of nearly every misguided conversation happening in AI governance right now.

Autonomous means self-governing. Not fast. Not capable. Not impressive. The system decides what it does today. The system sets its own goals. The system turns itself on. The system determines its own authorization. The system selects its own tools, processes, services, and schedule. The system decides, on its own initiative, that it doesn’t feel like doing something today.

Show me that system. I’ll wait. All systems today are heteronomous.

What we have instead is sophisticated automation. Well-designed, increasingly capable, genuinely useful automation. But automation is not autonomy.

Breaking Down “Goal-Seeking”

One term that some people point to is actually an interesting discussion. “Goal-seeking.” And it’s worth taking apart carefully, because there are two distinct words hiding inside it with very different implications.

“Goals.” And “seeking.”

Start with goals. Every system has one. That’s why it was built. A thermostat has a goal: to maintain the target temperature. A recommendation engine has a goal: to surface content that keeps users engaged. An AI agent has a goal: to accomplish the task it was designed to accomplish. Goals are not evidence of autonomy. Goals are design specifications. They are human decisions, encoded into systems by humans, for human purposes.

When a system breaks, it stops fulfilling its goal. The thermostat fails. The recommendation engine serves irrelevant content. The agent returns errors. The goal didn’t change. The system’s capacity to pursue it did. Goals belong to the humans who set them. Systems are the mechanisms by which humans pursue their goals.

Now the complicated word. Seeking.

Seeking implies intent. It implies that the system is reaching toward something, not just executing a function. And this is where the conversation gets interesting, because seeking does describe a real aspect of how modern AI systems operate. They don’t follow a fixed script from A to B. They navigate. They consider options. They find paths.

But here is what seeking does not imply: that the system is reaching toward its own goals.

An AI agent is seeking to fulfill a goal set by a human. The seeking is real. The path may be flexible, adaptive, and opaque to the person who requested the outcome. The destination is still where the human told it to go. The system is not pursuing its own ambitions. It is navigating toward a human-specified objective, and everything in between is the path.

The Path Is Not the Point

Here’s where the confusion lives. The path from goal assignment to goal completion can be complex. Genuinely complex. Complex enough that the person who set the goal has no visibility into the steps the system took to achieve it.

Vibe coding is the clearest current example. Someone who doesn’t know how to program uses an AI tool to build software. They specify an outcome. The system produces code. The person has no meaningful understanding of how the system got from their description to a working application. The process is completely opaque to them.

It might feel like autonomy. But unfamiliarity is not autonomy.

What happened is that a human specified a goal, and a system automated a process the human was unfamiliar with. Unfamiliarity is not autonomy. The magic of the experience is real. The perception it generates about what the system is doing is not.

A person who doesn’t know how an internal combustion engine works, sitting in the back seat, isn’t experiencing autonomous transportation. They’re experiencing a technology they don’t understand. The car still has a driver. The agent still has a human who set the goal. The opacity of the mechanism doesn’t change the nature of the system.

Consider the peanut butter and jelly sandwich. The goal is the sandwich. The process has some logic to it: you need bread before you spread anything, you need a utensil, and if you are a decent person, you wipe the knife between jars, or use separate knives. The rules of assembly have flexibility. You might spread the jelly first. You might use a spoon instead of a knife. The path varies, but the logical components for reaching the goal have already been solved.

The goal doesn’t change. And critically, nobody handed the goal to the sandwich. A human wanted a sandwich. The human set the goal. The process served the goal.

Scale that up to a complex multi-step agentic workflow. The goal was set by a human. The intermediate steps are the path. The path may be elaborate. The path may be opaque. The path does not make the system autonomous.

Self-Governance Is the Only True Autonomy

Governance, in the context of autonomy, doesn’t mean an external party applying rules to the system. That’s just governance. Autonomy means the system governs itself. Self-governance. The system determines its own limits. Sets its own constraints. Decides what it will and won’t do based on its own values and priorities.

That is the meaningful definition of an autonomous agent. And that agent does not exist in production anywhere today.

What exists is automation. Fast, capable, increasingly opaque automation that can navigate complex paths toward human-specified goals. Automation that benefits enormously from governance. Automation that creates real risks when it runs without adequate oversight. Automation is worth taking seriously.

Just not autonomous.

Call it what it is. The governance conversation gets much cleaner when we stop governing the thing we imagined and start governing the thing we built.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 30 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.