Agentic AI Controls: A Working Definition

What should count as an agentic AI control? It is a rule or mechanism that limits, checks, approves, records, or stops an agent action before that action changes something outside the model. The useful definition is narrower than “AI governance” and broader than “model security”. It has to cover a payment release, a customer record update, a deleted file, and a changed access right.

Agentic AI controls, in risk and engineering language

Risk teams use “control” to mean the thing that makes a risk tolerable. Engineers often hear the same word as a gate, a check, a validation, a limit, or a log. Both readings are needed for agents, because an agent does not merely produce an answer; it may also act.

MIT Sloan puts one limit on the discussion at the start: there is no universally agreed definition of agentic AI. That matters. If the underlying term is still fluid, “agentic AI controls” should be treated as a working definition, not as a fixed standard term.

While there isn’t a universally agreed upon definition of agentic AI, there are broad characteristics associated with it.

— MIT Sloan, Agentic AI, explained, 18 February 2026

A practical definition is this: agentic AI controls are the policy, technical, security, supervision, and evidence measures that constrain what an autonomous agent can access, decide, and do. That definition is not neutral. It deliberately treats the agent action as the controlled event.

That is the split from older model governance. A model governance control may ask whether a model produced an acceptable prediction. An agentic control asks whether the agent was allowed to send the offer, change the record, call the tool, or continue without a human decision. Attentive states the contrast in almost those terms, saying that traditional AI governance controls what models predict, while agentic AI governance enables what agents do autonomously.

This does not make output controls worthless. A content filter may still stop a bad message. A test may still detect drift. A human review may still catch a proposed action before it leaves the queue. But if the agent can touch the order system, the customer file, or the payment workflow, then the control set cannot stop at the answer the model wrote.

MIT Sloan’s implementation guidance names regulatory controls, guardrails, outcomes, and key performance indicators as areas to address when deploying agentic AI. It also says agentic AI presents data quality, governance, trust, and security challenges, and warns against adoption without understanding capabilities or creating a formal strategy and risk management framework. Those are governance statements. They do not tell an engineer exactly where to put a check before a credit limit changes.

For an engineer, the same definition needs to be operational. A control is the thing that answers one narrow question at the moment of action: may this agent, in this context, do this specific thing to this specific resource now? If the answer is yes, the control should leave enough evidence to defend that yes later. If the answer is no, it should stop the action, route it for approval, or force a safer alternative.

That is why the term “agentic AI controls” should include limits before execution and records after execution. A policy with no runtime effect is advice. A runtime gate with no record is hard to audit. A record that says only “the agent acted” is not enough if the dispute is whether the agent was allowed to act.

The boundary is also important. Agentic AI controls do not prove that an agent is useful. They do not prove that a model is accurate. They do not prove that a vendor is safe. They only reduce, stop, or evidence specified failure modes around access, decisions, actions, supervision, and security.

For more detail on that action boundary, Who Authorizes an MCP Tool Call? is the closest companion reading. For threat framing rather than definition, use the authorization-failure companion note.

An agentic AI control framework without pretending it is a new audit regime

An agentic AI control framework should map risks to controls and controls to evidence. It should not start by inventing a parallel compliance universe. That is the common failure: the organisation writes a polished AI policy, but the agent still has a path to update a customer record without a decision record that a reviewer can understand.

Attentive defines agentic AI governance as a framework of policies, controls, and monitoring systems that define what autonomous agents can do, how they access resources, and how their actions are supervised, audited, and controlled in real time. BigID defines the same territory around access to sensitive data, decisions, action, real-time monitoring, policy enforcement, and data-aware controls. Those definitions are useful because they name the verbs: access, decide, act, supervise, audit, control.

This shift from prediction to action demands governance frameworks that operate at runtime, provide granular control over agent capabilities, and maintain complete audit trails.

— Attentive, Agentic AI Governance: Why Autonomous Agents Need Different Controls — Blog, 14 April 2026

A working framework can therefore be organised around the agent action lifecycle. First, define the actions the agent may attempt. Second, define the resources those actions may touch. Third, define the conditions that allow, stop, or escalate the action. Fourth, define the evidence that must exist after the action. The framework is not the spreadsheet. The framework is the translation between the business risk and the runtime decision.

Digital Applied states the opposing temptation plainly: when SOC2 first meets agentic AI, teams may invent a new compliance regime, an “AI controls framework” beside SOC2 with its own evidence stream. The same source says the framework is the explicit translation between existing control language and the agentic-AI implementation that satisfies it. That is a better discipline.

For example, take a control about access to sensitive data. In ordinary control language, the organisation may say that only authorised users may access restricted customer data. In agent language, that becomes more concrete: the agent may retrieve only the fields needed for the permitted task, for the permitted customer, under the permitted workflow, and the action must be recorded in a way that links the request, decision, and result. The wording changes because the actor changed.

Now take a control about change management. In ordinary control language, the organisation may say that changes to production workflows require approval. In agent language, that becomes a rule about whether the agent may alter a workflow, generate a configuration change, or submit a change request without a human decision. A monitor that only observes the change after it happens is not the same control as a gate that checks authority before the change happens.

Digital Applied also says the Trust Services Criteria were written framework-agnostic and that almost every control has a clean agentic-AI translation. That claim should not be stretched too far. It does not prove that every audit framework already covers every agent risk. It does support a conservative approach: translate existing controls first, then add agent-specific controls where the translation fails.

The control framework should have at least these named parts:

• Action inventory. The organisation should identify what the agent can do outside the model: send a message, update a record, approve a refund, open a ticket, change access, or call another system.
• Resource limits. The organisation should state which data, systems, customers, accounts, files, and workflows the agent may touch.
• Decision rules. The organisation should define when the agent may proceed, when a human must approve, when the action must be escalated, and when it must be blocked.
• Runtime checks. The organisation should put the rule at the point where the agent tries to act, not only in a policy document.
• Evidence records. The organisation should preserve what was requested, what was decided, why it was decided, and what happened next.
• Review cadence. The organisation should review the control evidence often enough to detect drift, overreach, and missing approvals before an audit or incident forces reconstruction.

This list is deliberately plain. It is not a certification scheme. It does not claim that a particular auditor will accept a particular artefact. It gives the risk owner and the engineer a shared vocabulary for turning “control the agent” into work items.

For audit-style framing, AI Agent Accountability Framework: Financial Services is relevant even outside financial services. For the broader collection of enterprise arguments, use Enterprise.

Agentic AI security controls at the action boundary

Security controls for agentic AI are a subset of agentic AI controls. They focus on protecting the agent, the systems it touches, and the paths by which it acts. They do not cover every governance concern. A brand approval rule may be a control, but it is not always a security control.

Microsoft defines agentic AI security as practices, controls, and tools that help organisations keep agents safe, reliable, and aligned with intended goals. Microsoft also defines it as the set of practices, controls, and tools used to protect autonomous AI systems that can plan, make decisions, and take actions with minimal human input. The second definition is more useful for control design because it names planning, decisions, and action.

Agentic AI security is the set of practices, controls, and tools used to protect autonomous AI systems that can plan, make decisions, and take actions with minimal human input.

— Microsoft Security, What Is Agentic AI Security?

Palo Alto Networks says that, in practice, securing agentic AI means placing controls at each boundary in the loop. Vectra says understanding the architecture of agentic AI systems reveals where security controls must be applied. Those statements point in the same direction: do not protect only the model layer.

Vectra is explicit that traditional AI security controls focused on the model layer are necessary but insufficient for agentic systems. That is a useful sentence for a risk register. It allows the team to keep model security in scope while rejecting the claim that model security is the whole control set.

The agentic AI security controls that matter most are the ones nearest to consequence. If an agent drafts an email and a person sends it, the control question is different from the case where the agent sends it directly. If an agent recommends a refund and a service representative approves it, the control question is different from the case where the agent releases the refund itself. The point is not to ban autonomy. The point is to decide where autonomy ends.

Airlock Digital describes a usage-control pattern in which commands are evaluated before execution, giving organisations control over what agentic agents can do and steering them to operate within approved boundaries. It also says its Agentic Steering communicates the policy decision directly to the agentic agent, with context or an approved alternative, so the agent can adjust its approach and continue within company-defined boundaries. Those are vendor claims, not neutral standards. They are still useful because they express the control location: before execution.

OWASP Agentic Security Initiative describes its Agentic AI threats and mitigations document as the first in a series of guides providing a threat-model-based reference of emerging agentic threats and mitigations. That is not an audit framework. It is a threat-model reference. Treat it as a way to find failure modes, not as proof that a specific payment release was authorised.

A security control set for an agent should therefore answer practical questions. Can the agent reach the sensitive system directly? Can it ask another tool to reach it? Does the action require a person? Does the approval cover this action or only the general task? Is the agent allowed to use the data it found for this purpose? Is there a record that shows the answer?

The last question is where security and compliance meet. A security team may care most that a bad action was blocked. A compliance team may also need to prove that the good action was permitted. If the system cannot show the difference, the organisation is left with a story rather than evidence.

For related security taxonomy, OWASP's Top 10 for Agentic Applications, Read by a Risk Officer gives a risk-owner reading. For the broader guardrail vocabulary, use Guardrails.

What this means if you have to produce evidence

Evidence changes the definition of a control. A policy may say the agent must not delete regulated records. An evidence record must show whether this attempted deletion was allowed, stopped, escalated, or approved. The gap between those two sentences is where many control designs fail.

BigID says agentic AI governance is used to monitor AI decisions, control data access, enforce compliance policies, and reduce risk across autonomous AI systems. Attentive says agentic AI governance includes how agent actions are supervised, audited, and controlled in real time. The shared point is not merely monitoring. It is monitoring tied to access, decisions, action, audit, and control.

Evidence should not be an afterthought assembled from chat logs and system traces. If the disputed event is a changed credit limit, the organisation needs to know what the agent requested, what control assessed it, what rule or approval allowed it, and what change occurred. A transcript alone may explain the agent’s reasoning. It will not necessarily prove authority.

Digital Applied says a modest translation table paired with continuous evidence capture beats a written AI-controls framework that requires manual collection before every audit cycle. The sentence is vendor commentary, not a rule. Its value is that it points to a hard truth: evidence is cheaper when the control creates it as part of the action.

There is also a limit. Evidence does not make a bad rule good. If the rule allows an agent to refund any customer without review, perfect evidence only proves that the bad rule operated. Evidence supports accountability. It does not substitute for judgement.

A useful evidence record for an agent action should be able to answer these questions:

• Actor. Which agent attempted the action, and under which assigned scope?
• Action. What did the agent try to do: send, delete, update, approve, retrieve, or change?
• Target. Which account, customer, file, workflow, record, or system would be affected?
• Context. What task, user request, business process, and data context surrounded the action?
• Decision. Was the action allowed, blocked, escalated, or sent for human approval?
• Reason. Which policy condition or approval explains the decision?
• Outcome. Did the downstream system accept the action, reject it, or change something?
• Review. Who or what reviewed the record later, and what exception handling followed?

That list is not derived from one named standard. It is the minimum shape of an accountable action record. An auditor, investigator, risk owner, or engineer may ask for more. They should rarely accept less for an agent that can change money, access, or records.

The most important design choice is where the evidence is created. If it is created before and during the action, it can show the decision. If it is reconstructed after the action, it can easily become a narrative. Narratives are useful in incident reports. They are weaker as control evidence.

Practical steps for defining agentic AI controls

1. Name the agent actions. Do not start with “AI risk” as a category. Start with the verbs the agent can perform: retrieve, send, change, approve, delete, create, route, escalate, or execute.
2. Separate recommendation from action. A recommendation to refund a customer is not the same controlled event as releasing the refund. The control should attach to the point where the outside system changes.
3. Map each action to a resource. A control that says “customer data” is too broad for runtime use. Define the actual records, fields, accounts, files, or systems the agent may touch.
4. Define allowed, approval, escalation, and block conditions. A control framework that cannot express those outcomes will struggle when the agent reaches a sensitive step.
5. Put the check before the consequence. A monitor that alerts after a record is deleted may be useful, but it is not the same as a control that decides whether deletion may proceed.
6. Record the decision, not only the event. A log that says an update happened is weaker than a record that says the update was requested, assessed, permitted, and completed.
7. Translate existing controls before inventing new ones. Access control, change control, approval control, data handling, and monitoring language can often be restated for agent actions before a new AI document is needed.
8. Test refusal paths. A control is not only proven by allowed actions. It should also show that the wrong action, wrong resource, wrong context, or missing approval fails safely.
9. Review evidence with the people who will rely on it. Engineers may accept a technical trace. Risk owners may need a policy explanation. Compliance may need both in one record.

The narrow position is that agentic AI controls should be runtime permissions with evidence, not policy statements that sit beside the agent; GREENLIGHT shows that permission-step framing, and the product demonstration is for teams comparing it with their own control design.

Crelis is patent-pending. It holds no SOC 2, ISO 27001 or ISO 42001 certification, and claims none.

Related reading

• Who Authorizes an MCP Tool Call? — the same control question applied to tool calls.
• Authorization failures in agentic systems — a threat-led reading of agent authorization failure.
• OWASP's Top 10 for Agentic Applications, Read by a Risk Officer — a control-owner view of application risk.
• Guardrails — the topic archive for guardrail and control language.