AI agents are being given greater authority to interact with applications, access data and take actions across enterprise systems. For CIOs, that creates a difficult control problem: By the time an agent does something it shouldn't, is it already too late to stop it?
Not necessarily.
meshIQ launched AgentIQ this month, saying the governance tool can evaluate an agent's requested action and stop it before it reaches an enterprise system. The larger issue is where that kind of runtime control sits in the execution path -- and how much of the execution chain remains outside its reach.
That makes the CIO question less about whether agents can be stopped at all and more about where enterprises can still intervene before an action becomes irreversible.
Enterprises can place controls between an agent's decision and the execution of an action, allowing a request to be inspected, approved or denied before it reaches another system. But with existing enterprise controls such as identity and access management (IAM) and privileged access management (PAM), how much of this is new?
"Runtime enforcement is not a replacement for IAM, PAM, workflow approvals or policy engines," said Anushree Verma, senior director analyst at Gartner. "It is an agent-aware control layer that applies those ideas at the moment an AI agent proposes an action, using the action's full context and parameters."
That distinction is becoming more important as agents move beyond retrieving information and begin taking actions on users' behalf.
Finding the moment before an agent acts
The opportunity to intervene exists after an agent has proposed an action but before the requested tool call or other external action executes, Verma said.
Enterprises can establish control points at several places in that process, including when an agent invokes a tool, at an API or enterprise gateway before a request reaches a SaaS application or through human approval requirements, Verma said. Separate recovery controls can also exist at the application or transaction layer after execution when an action remains reversible, she added.
According to meshIQ, AgentIQ places its control point before an agent's requested action reaches the enterprise system. "Rather than simply determine if a particular tool is allowed, AgentIQ evaluates the proposed action against enterprise policy using its runtime parameters and risk," Navdeep Sidhu, CEO of meshIQ, said.
Based on that evaluation, an action can be allowed, paused for confirmation, escalated for approval or denied. Sidhu said AgentIQ can intercept actions within a governed execution path before they execute.
A key distinction is between giving an agent access to a capability and determining whether a particular use of that capability should be permitted. An agent could legitimately have access to a tool while a specific action still requires additional approval.
Sidhu said AgentIQ evaluates what the agent is attempting to do, the parameters involved and whether the action complies with enterprise policy. Existing identity and access controls remain responsible for determining access to enterprise systems; AgentIQ is intended to augment, not replace, IAM, he said.
Old controls meet unpredictable execution paths
That raises the harder question for CIOs evaluating a growing number of agent governance products: whether runtime enforcement represents a fundamentally new enterprise control or applies familiar security mechanisms to a new execution model.
Traditional IAM establishes who or what can access a resource, while workflow approvals and policy engines can establish additional conditions around how actions proceed.
What changes with agents is that the complete execution path might not be known in advance.
An agent can determine which system or capability to use, what parameters to apply and what step to take next as a workflow unfolds. Sidhu said that makes runtime policy decisions important even when an agent already possesses legitimate access to a system.
Scale adds another consideration. AI agents can act at a much larger scale than an individual employee performing an action manually, making runtime enforcement more critical, said Tom Coshow, vice president analyst at Gartner.
Authorization should consider several pieces of context, Gartner's Verma said. When an agent acts on behalf of a user, that user's authority helps establish what the agent can legitimately do; system-initiated agents might instead operate under a service or workload identity. The agent's identity, requested action and surrounding context then determine whether the action should be permitted at that particular moment.
The challenge becomes more complicated when the first agent is not the last actor in the chain.
The control boundary matters
When one agent invokes another agent, tool or workflow, the next invocation should be treated as a new authorization boundary rather than inheriting the first agent's authority, Verma said.
"The safest architecture should be that every external effect passes through a separately enforceable control point, with provenance showing exactly which user, agent, delegation and policy decision led to it," she added.
But enforcement depends on where those control points exist.
According to Sidhu, AgentIQ can govern a third-party tool call before it leaves an agent. Once the request reaches the external platform, however, the platform itself sits outside AgentIQ's enforcement layer.
Visibility into any additional actions triggered within that platform depends on what it exposes through its API or Model Context Protocol (MCP) response, Sidhu said.
The same issue emerges in multi-agent workflows. If two agents registered with AgentIQ interact through a tool call, the platform can apply each agent's respective policies as execution moves between them, Sidhu said. Direct agent-to-agent handoffs can also be governed independently, although meshIQ is still developing a common trace connecting those handoffs.
That boundary is significant for CIOs because stopping one request does not necessarily mean controlling everything that can happen downstream.
"The boundary ends wherever the agent's action produces an effect that is not mediated by a controllable system," Verma said.
Some applications provide a transaction or rollback window after an action executes, Verma explained. Without such a mechanism, however, an action may not be reversible once executed.
The boundary ends wherever the agent's action produces an effect that is not mediated by a controllable system
AgentIQ, for example, is not a rollback system, Sidhu said. If an action has already been authorized and completed, the runtime governance layer cannot reverse it.
Prevention is different from detection
That distinction between preventive and detective controls matters as vendors add agent governance capabilities.
Monitoring an agent, generating an alert or recording its actions can help an enterprise investigate what happened. Those capabilities do not necessarily demonstrate that a system could have prevented the action.
CIOs evaluating runtime controls should require vendors to show that an unauthorized external effect does not occur, Verma said. They should also expect the policy decision to be explainable and attributable and seek evidence from the target system itself.
"If a vendor can show only alerts, policy recommendations, sandboxing without mandatory mediation or post-execution rollback, it is offering detection or mitigation, not preventive runtime control," Verma said.
What CIOs should test in runtime agent controls
Runtime governance claims can describe distinct capabilities. CIOs should test where a control sits and what it can prevent.
Enforcement point: Is every relevant action forced through the policy check before it reaches the target, and what happens if the control is unavailable?
Decision context: Does the control evaluate the user, agent identity, delegation, requested action and runtime parameters?
Handoffs: Is each agent, tool or workflow invocation treated as a new authorization boundary?
Proof of prevention: Can the vendor show that an unauthorized external effect did not occur, rather than merely alerting afterward?
Boundary and rollback: What happens once an action crosses the control layer, and can the target system reverse it?
For CIOs, the practical test is whether every consequential action must pass through the control before execution. Anything that can bypass it remains outside the control boundary.
Liz Hughes is an award-winning editor and writer covering AI and emerging technology and the former editor of AI Business and IoT World Today.
