The vocabulary we use for governance has not kept pace with agentic AI.

We talk about guardrails, which frame governance as restriction rather than design. We talk about safety, which emphasizes avoiding harm rather than enabling appropriate action. We talk about alignment, which focuses on training intent rather than runtime behavior.

None of these terms captures what governance for agentic AI actually requires: an intelligent layer that defines what AI should do, operating with the same sophistication we apply to what it can do.

The Missing Half

Watch any presentation about agentic AI and a pattern emerges quickly. Here is what the system perceives. Here is how it reasons. Here is how it plans. Here is what it can do. (Of course these systems don’t perceive, reason or plans in a human sense.)

Capability dominates the conversation.

Governance appears briefly, if at all. A slide on safety. A mention of human oversight. A reference to responsible AI principles. Then the discussion returns to capability.

This imbalance mirrors how organizations build. Teams spend months architecting agent capabilities. Governance arrives later, often after something breaks or compliance starts asking uncomfortable questions.

The outcome is predictable. Systems with impressive capability but unclear authority boundaries. Agents that can act without defined limits on when they should. Sophisticated reasoning wrapped in unsophisticated oversight.

Gartner estimates that more than 40 percent of enterprise agentic AI projects may fail by 2027 due to rising costs, unclear business value, or inadequate risk controls. Capability is rarely the cause of failure. Governance is.

Can Versus Should

The distinction is simple and fundamental.

Agentic AI answers one question: What can this system do?

Nomotic AI answers a different one: What should this system do?

These questions require different architectures. Capability is about potential. Governance is about the appropriate exercise of that potential. Capability asks what is possible. Governance asks what is permitted, under what conditions, and within what limits.

A system can be extraordinarily capable and disastrously governed. It can reason brilliantly and act inappropriately. It can plan efficiently toward goals it should never pursue.

As capability increases, so does the risk of operating without governance. Power without judgment scales poorly.

Why Traditional Approaches Fall Short

Traditional governance was designed for human-speed decisions. Annual policy reviews. Quarterly audits. Updates triggered by incidents. Humans make decisions slowly enough that periodic oversight can keep pace.

Agentic AI does not work that way.

Agents make thousands of decisions per minute. They adapt to conditions in real time. They interact with other agents in ways that produce behavior, often without a clear chain of causality.

Static rules cannot govern dynamic systems. A policy written six months ago cannot anticipate new conditions. Context-blind enforcement cannot distinguish actions that differ only by circumstance.

Human oversight helps, but it does not scale. OWASP correctly notes that human-in-the-loop approval quickly becomes ineffective due to cost, delays, and approval fatigue. You cannot place a human checkpoint on every agent decision. There are too many decisions, and they are made too quickly.

This is why governance itself must become intelligent. It must operate at runtime and adapt as conditions emerge. It must understand context rather than simply matching patterns. It must use AI to govern AI.

The Nomotic Layer

Nomotic AI provides vocabulary and structure for this governance layer. The term derives from the Greek word nomos, meaning law or governance.

Agentic systems act based on capability. Nomotic systems determine, guide, and enforce what those systems should do. They function as an intelligent oversight layer.

Nomotic AI evaluates whether that action should occur.

Neither layer works alone. Actions without laws create disorder. An unguided agent can pursue outcomes that violate policy or produce results no one can explain. Laws without action accomplish nothing. Rules only matter when something follows them.

Effective AI deployment requires both layers. Capable systems operating within intelligent governance structures produce outcomes that are useful, predictable, and accountable.

What the Nomotic Layer Requires

A governance layer for agentic AI must meet several requirements.

It must be intelligent. Governance cannot rely solely on static rules or pattern matching. It must understand context and intent, reasoning about what agents are attempting and why.

It must be dynamic. Trust should increase when behavior is consistent. Authority should contract when anomalies appear. Governance frozen in time cannot respond to reality.

It must operate at runtime. Pre-deployment configuration is insufficient. Post-incident review is too late. Governance must participate before, during, and after execution.

It must be contextual. The same action may be appropriate in one situation and dangerous in another. Accessing data for a legitimate workflow is not the same as access following suspicious input. Context determines appropriateness.

It must be transparent. Decisions must be explainable and auditable. If governance cannot explain why an action was permitted or denied, accountability becomes impossible.

It must be ethical. Compliance is necessary but insufficient. Actions should be justifiable on principle, not merely allowed by procedure.

It must preserve accountability. Every rule should trace to an owner. Every authorization should trace to a responsible human. AI systems cannot be accountable. Humans must remain in the chain.

These characteristics describe what governance for agentic AI must become. The term Nomotic AI provides a name for systems that embody them.

The Conversation We Need

Every discussion of what AI systems can do should include what they should do. This is not about slowing progress. It is about ensuring progress occurs within structures that support accountability.

Vocabulary matters because it shapes thinking. Guardrails suggest restriction. Nomotic systems suggest lawful enablement. Safety suggests avoiding harm. Governance suggests defining appropriate behavior.

The framing determines the architecture.

Building agentic AI without its governance counterpart is building capability without accountability. Focusing only on what AI can do is having half a conversation.

The agentic layer asks what is possible. The nomotic layer asks what is appropriate.

Both questions deserve sophisticated answers.

The Pairing

Agentic AI reshaped how the industry talks about capability. New vocabulary emerged. New architectures followed. Entire categories of systems became possible to describe and build.

Nomotic AI offers the same evolution for governance. Not a product to purchase, but a category to understand. Not a competing framework, but a complementary layer. A vocabulary for discussing what AI should do.

The industry has spent years building increasingly capable AI systems. Governance has not kept pace.

That gap is not sustainable. As agents become more capable, intelligent governance becomes more urgent. As AI takes more actions in the world, deciding which actions are appropriate becomes more consequential.

Every “can” requires a “should.”

The question is no longer whether you are building capability.

Are you building governance too?

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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 40 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.