I get pushback. A lot of it. Every time I say that no AI system today is autonomous, someone in the room shifts uncomfortably and returns with an opinion. The objection is usually a variation on the same theme: “Well, our agents have autonomy within defined parameters.” Or the more polished version: “We use bounded autonomy.”

Bounded autonomy. It’s a term engineers love because it sounds rigorous and precise. It acknowledges that the system has limits while still claiming the word that makes the marketing work. But what bounded autonomy actually confirms, every single time it’s used, is that a human is controlling what the system does. That isn’t autonomy. That’s heteronomy.

I’m also frequently told that autonomy and governance are different things. That autonomy is about what a system can do, and governance is about the rules we place on it. As if these are separate concepts operating in parallel. This, more than anything, demonstrates the depth of confusion about what autonomy, or more precisely, what automation actually means.

Autonomy is governance. It is self-governance. It is the ability for a system to regulate itself, to determine its own rules of behavior through internal reasoning. The idea that you can separate autonomy from governance reveals a fundamental misunderstanding. When people say “our system is autonomous but governed,” they’re describing a system that operates under external regulation while performing tasks of varying duration and complexity. How long a system runs or how capable it is at a given task does not determine whether it’s autonomous. A system that executes a complex workflow for six hours is not more autonomous than one that completes a simple task in six seconds. The duration of activity and the accuracy of capabilities are operational metrics, not indicators of self-governance.

I’ve made this argument before. I’ll keep making it. But a conversation yesterday introduced a distinction I hadn’t fully explored, and it’s worth thinking through. Not because it changes my conclusion, but because it sharpens it.

The question was simple. Is there a difference between bounded and boundary?

Two Kinds of Constraint

At first glance, the words feel interchangeable. But they describe fundamentally different kinds of restriction.

Bounded suggests restrictions on action. What the system can do. You can perform these tasks, but not those. You can generate a recommendation, but not execute a transaction. You can draft a response, but not send it. The system’s capabilities are limited, its range of permissible behavior is defined, and anything outside that range is either blocked or escalated. The constraint lives inside the system’s operational logic.

Boundary suggests restrictions on access. Where the system can go. You can operate within this dataset, but not that one. You can interact with these APIs, but not those. You can function within this domain but not wander into another. The constraint isn’t about what the system does. It’s about the territory it’s allowed to occupy. The fence around the field, not the rules inside it.

The conversation I had leaned into the idea that “autonomy doesn’t mean unlimited.” And there’s a reasonable instinct there. After all, human autonomy isn’t unlimited either. We operate within laws, social norms, and physical constraints. A cow can graze freely until it hits a fence. A person in a library can read or study, but they can’t throw a party. Autonomy, the argument goes, has always coexisted with limits.

But this is where the analogy breaks down in ways that matter for AI systems.

The Cow and the Library

Consider the cow. It grazes wherever it wants within the pasture. It chooses when to eat, where to walk, and when to rest. The fence defines the boundary of its access, but within that space, the cow’s behavior is self-directed. The fence doesn’t tell the cow what to eat or when to move. It only defines where.

Now consider the library. You walk in. You can read. You can study. You can browse. But you can’t shout, play music, or host a celebration. These aren’t access restrictions. They’re action restrictions. Your behavior is bounded by rules that govern what you’re allowed to do inside the space, regardless of how much space you have access to.

These feel like two different flavors of the same thing. One restricts territory, the other restricts behavior. Both are forms of constraint. And defenders of “bounded autonomy” will point to both as evidence that autonomy can coexist with limits. The cow is still autonomous within the fence. The library patron is still autonomous within the rules.

But here’s where I land, and where I think the conversation needs to be more honest.

The Librarian Says Shhh

Imagine you’re in the library. You’re reading, minding your own business, and your phone rings. You answer it. The librarian looks up and tells you to be quiet. In that moment, your self-governance has been overridden. You didn’t decide to stop talking because of your own internal reasoning. You stopped because an external authority imposed a rule on your behavior. You complied with governance that originated outside of yourself.

That’s heteronomy. And it doesn’t matter how much freedom you had before the phone rang. The moment an external constraint dictates your behavior, the system of self-governance is interrupted.

Now apply this to AI systems. Whether we restrict the actions a system can take or the territory it can access, the result is the same. The system is operating under externally imposed rules that it did not create, cannot override, and has no capacity to reason its way around. The bounded system can’t decide to perform an unauthorized action because it determines that the action is necessary. The boundary-limited system can’t decide to access restricted data because it concludes the data is relevant. In both cases, the system’s behavior is dictated by something outside itself.

The minimum viable output in either scenario is a heteronomous environment. Whether you constrain what the system does or where it goes, you’ve placed governance on the system that originates with humans, not with the system itself. And that is the definition of heteronomy.

Why the Distinction Still Matters

If bounded and boundary both lead to heteronomy, why bother distinguishing them?

Because the distinction reveals how we talk ourselves into believing these systems are something they’re not. “Bounded autonomy” sounds like a reasonable middle ground. It sounds like we’ve found the sweet spot between full autonomy and full control. But it isn’t a middle ground. It’s full control described in language that preserves the illusion of autonomy. Or simonomy.

And this matters for governance. If you believe your system has bounded autonomy, you govern it as if it might surprise you, as if it has some degree of independent judgment that needs to be monitored. But if you recognize the system is heteronomous, you govern it as what it is: a tool that executes within the parameters you’ve defined. The governance model, risk assessment, accountability structure, and customer-facing expectations all shift when you’re honest about what the system actually is.

The cow doesn’t choose to stay in the pasture. The library patron doesn’t choose to be quiet. And the AI agent doesn’t choose to operate within its approved boundaries. Choice requires self-governance. Everything else is compliance.

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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.