There is a temporal gap at the center of AI governance, and almost nobody is building to close it. Nomotic is.

Most governance for AI systems operates at one of two moments: before execution begins, or after it ends. Pre-execution governance evaluates requests, checks permissions, and applies policy filters. Post-execution governance reviews outcomes, logs results, and flags anomalies for human review. Both are necessary. Neither is sufficient.

The actual runtime, where actions occur, consequences accumulate, and failures cascade, remains largely ungoverned. Agents act in milliseconds. Humans review in minutes or hours. That mismatch is not a minor inconvenience. It is a structural vulnerability that grows more dangerous as AI systems become more dynamic.

The Speed Problem Is an Architecture Problem

Consider what happens when an AI agent encounters an edge case during execution. In a traditional software system, the code follows a deterministic path. In an agentic system, the agent reasons, adapts, and may take actions that no one anticipated at configuration time. A policy written six months ago cannot account for conditions that did not exist then.

The standard response is human-in-the-loop oversight. But as OWASP has correctly noted, human-in-the-loop approval becomes ineffective at scale due to cost, latency, and approval fatigue. You cannot place a human checkpoint on every agent decision when agents are making thousands of decisions per minute. The math simply does not work.

This is not an argument against human oversight. It is an argument for governance that can operate at execution speed while keeping humans in the authority chain. The question is not whether humans should govern AI, but how governance architectures can make that authority real when decisions happen faster than any human can review them.

Static Rules Cannot Govern Dynamic Systems

Most AI governance today is essentially static. Define a policy. Encode it as a rule or filter. Apply it uniformly. Review periodically. Update when something breaks.

This works for predictable systems. It fails for agentic ones. The same action may be entirely appropriate in one context and dangerous in another. Accessing customer data to fulfill a support request is routine. Accessing customer data after receiving adversarial input is a security incident. The action is identical. The context makes it safe or catastrophic.

Context-blind enforcement cannot make this distinction. Pattern matching cannot either. What is needed is governance that understands the situation it is governing. Systems that can evaluate intent, assess risk in real time, and adjust responses based on what is actually happening rather than what a policy author imagined might happen.

This implies something uncomfortable: governance itself must become intelligent. Not in the sense of replacing human judgment, but in the sense of being able to apply human-defined principles to situations humans have not individually reviewed. The alternative is either accepting an ungoverned runtime or throttling AI systems to human decision speed, which eliminates most of the value of deploying them.

Trust Should Be Earned, Not Configured

There is a binary quality to how we currently trust AI systems. We either deploy them or we do not. We either grant access or we withhold it. Configuration happens at deployment. Trust is assumed from that point forward.

This is not how trust works in any other domain. We do not give a new employee the same authority as a ten-year veteran on their first day. We do not grant contractors the same access as internal team members without additional oversight. Trust is calibrated, earned through demonstrated behavior, and adjusted when circumstances change.

AI governance should follow the same logic. An agent that has consistently operated within its boundaries might warrant expanded authority. An agent exhibiting anomalous behavior should face increased scrutiny automatically, not after a human notices something in a dashboard three hours later. Trust should be a continuous, evidence-based signal, not a one-time configuration decision.

I describe this as “nomotic,” governance from the Greek nomos, meaning law or rule, to distinguish runtime behavioral governance from the broader category of AI safety and compliance. The distinction matters. Safety asks whether a system is dangerous. Compliance asks whether it meets regulatory requirements. Nomotic governance asks a more operational question: is this specific action, in this specific context, something this agent should be doing right now?

The Counterintuitive Case for the Kill Switch

There is an irony in how organizations approach AI deployment. The teams most hesitant to grant agents meaningful authority are often the ones least able to intervene if something goes wrong. They restrict scope precisely because they lack confidence in their ability to course-correct at runtime.

The counterintuitive insight is that the ability to stop an agent mid-execution actually enables broader deployment. When you can interrupt a specific action, a specific agent, or an entire workflow without shutting down the whole system, the calculus changes. You do not need to anticipate every failure mode before deployment because you retain the authority to intervene when unexpected situations arise.

This is not a novel principle. Circuit breakers in electrical systems do not prevent the use of electricity. Interrupt authority in AI governance serves the same function. It transforms deployment from a leap of faith into a managed process where trust is extended incrementally and can be revoked instantly.

But building real interrupt authority is architecturally demanding. The governance layer must operate in parallel with execution, not just before or after. It must have real-time visibility into the agentic activities. It must have the technical capability to intervene, not just observe. And it must handle partial completion gracefully. Interrupting an action mid-stream without corrupting the state is harder than it sounds.

Most organizations have not made these architectural commitments. The governance layer that can actually interrupt is harder to build than the governance layer that merely advises. So most governance is advisory. And advisory governance, when it encounters a fast-moving failure, is commentary after the fact.

The Conversation We Are Not Having

The AI industry has a sophisticated vocabulary for capability. Agentic AI, tool use, chain-of-thought reasoning, multi-agent orchestration. These terms describe what AI can do with increasing precision.

The vocabulary for governance remains comparatively underdeveloped. Guardrails, safety filters, alignment, and responsible AI. These are useful concepts, but they are either too narrow, too vague, or too focused on preventing harm rather than enabling appropriate action. None of them adequately describes the intelligent, adaptive, runtime governance architecture that agentic systems require.

Gartner has estimated that more than forty percent of enterprise agentic AI projects may fail by 2027, not because of capability limitations, but because of rising costs, unclear value, or inadequate risk controls. The pattern is familiar from earlier waves of enterprise technology: the organizations that succeed will be the ones that treat governance as an architectural decision, not a compliance afterthought.

Every discussion of what AI systems can do should include what they should do. Every architecture for agent capability should have a corresponding architecture for agent governance. And that governance must operate where the actions happen, at runtime, in real time, and with the authority to intervene.

If you cannot stop it, you do not control it. And if you do not control it, calling it “governed” is generous.

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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.