Achieving Agentic AI Oversight Where You Didn’t Have It Before: A Framework for Enterprise Control
For most enterprises, the conversation around AI governance has focused on the tools their employees choose to use, the ChatGPTs, the Copilots, the publicly available models that show up in browser traffic and prompt logs. That conversation, while important, is no longer sufficient.
A different category of AI is now operating inside enterprise environments, and it is doing so largely without oversight. AI agents, autonomous systems that plan, decide, and act without waiting for human input, are already embedded across business operations at significant scale. According to Microsoft’s Cyber Pulse Report, 80% of Fortune 500 companies now have active AI agents built using low-code or no-code tools, with many of those agents described as unsanctioned, unobserved, or over-privileged.
The problem is not that organisations are using AI agents. The problem is that most do not yet have the agentic AI oversight, or the agentic AI framework, needed to know what those agents are doing, where they are operating, or what risks they are creating.
Why Agentic AI Is a Different Governance Challenge
AI agents are not simply a faster version of the AI tools your team already manages. They are categorically different in how they operate, and that difference has direct consequences for security and governance.
A chat-based AI tool waits for a human to type a prompt. An AI agent does not wait for anything. It executes multi-step workflows, makes calls to AI applications and external systems, takes actions on behalf of users, and moves at a speed that no manual monitoring process can match. McKinsey describes AI agents as “digital insiders”, entities that operate within systems with varying levels of privilege and authority, and notes that 80% of organisations have already encountered risky behaviours from AI agents, including improper data exposure and access to systems without authorisation.
The speed and autonomy that make agents productive are precisely what make them difficult to govern. As one cybersecurity leader put it: “Developers have embraced agents as part of daily workflows, but security teams lack the tools and visibility to keep pace. That mismatch is now the biggest enterprise risk of 2026.”
And the exposure is growing fast. Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. Organisations that do not build an agentic AI framework now are not simply behind, they are allowing an ungoverned environment to scale.
The Oversight Gap Most Organisations Are Not Talking About
Despite the scale of deployment, governance has not kept pace. Only 47% of organisations surveyed said they have AI-specific security controls in place, meaning more than half are deploying or allowing AI agents without dedicated policies or technical safeguards designed for the risks those agents introduce.
Many organisations lack even a basic inventory of autonomous agents and the systems they can reach, a foundational requirement for enforcing identity, privilege, and compliance boundaries.
This is the agentic AI oversight gap. It is not a theoretical future risk. It is the current state of most enterprise environments, and it is widening with every new agent that is deployed, enabled, or embedded in a SaaS platform without a formal security review. Without a structured agentic AI framework in place, organisations have no consistent basis on which to detect, assess, or respond to the risks these agents create.
The Agentic AI Framework Enterprises Need: Five Steps to Governance
Achieving agentic AI oversight is not a single action. It requires a structured agentic AI framework, a set of sequential, buildable capabilities that give organisations the intelligence and control they need at every stage of their agentic AI journey.
Portal26’s approach to agentic AI governance is built around a five-step framework: Discovery, Visibility, Risk, Enforce, and Value.
Step One: Discovery – Know What Is Running
You cannot oversee what you do not know exists. The starting point for any agentic AI framework is a complete and continuously updated inventory of every AI agent operating across your enterprise. For most organisations, this is not a list they currently have.
AI agents are not confined to a single part of the enterprise. They run on employee laptops, they operate within cloud and hyperscale environments such as AWS, Azure, and GCP, and they are embedded within SaaS platforms that employees use every day. Each of these locations represents a distinct vector through which ungoverned agents enter and operate within the enterprise.
Portal26’s Agentic AI Security feature discovers AI agents across all three of these locations, giving organisations for the first time a consolidated picture of what autonomous systems are active across their environment.
Learn More About AI Agent Disovery >
Step Two: Visibility – Understand What Agents Are Doing
Discovery tells you what exists. Visibility tells you what those agents are actually doing. These are not the same thing, and both are required for meaningful agentic AI oversight.
Portal26 surfaces the detail organisations need to move from a list of agents to a genuine understanding of their behaviour: the agent name, the underlying AI model it is using, the number of users associated with it, the volume of calls it is making to AI applications, and the specific prompts and responses passing between agent and model.
This is the level of visibility that allows a security or IT team to make an informed judgement about whether an agent is operating within acceptable boundaries, or whether it has drifted beyond them.
Step Three: Risk – Identify What Agents Are Putting at Risk
With discovery and visibility in place, organisations can begin to assess the risk their agents are creating, the third pillar of a functioning agentic AI framework. Portal26 uses 25+ purpose-built agentic AI risk detectors designed specifically for the threat patterns that autonomous systems introduce, including unsupervised access to internal systems, unauthorised financial transactions initiated by agents, production environment changes made without human review, and high volumes of AI application and tool calls without human oversight.
Two risk types in particular characterise the most common and serious agentic AI security failures. The first is agents with too much agency, systems that overstep their operational boundaries and take actions that exceed their intended remit. The second is rogue agents, systems making excessive calls to AI applications and tools, driving up costs and generating outputs without any human in the loop.
Portal26 surfaces a risk heatmap across the agent environment, allowing security teams to identify which agents carry the highest overall risk and drill down into the specific conversations and interactions generating each signal.
Steps Four and Five: Enforce and Value
A complete agentic AI framework does not stop at risk identification. The next steps, Enforce and Value, will extend Portal26’s capabilities to allow organisations to apply controls automatically and begin extracting strategic value from their agentic AI intelligence.
Why Organisations Cannot Afford to Wait
The instinct for many enterprises is to manage agentic AI governance later, once the technology matures, once there is more regulatory clarity, once internal priorities allow for it. The data suggests that instinct carries significant risk.
Deloitte’s 2025 Emerging Technology Trends study found that 35% of organisations have no formal agentic AI strategy at all. PwC’s AI Agent Survey found that trust dropped sharply for higher-stakes agentic use cases, with only 20% of respondents comfortable delegating financial transactions to AI agents, and only 22% comfortable with autonomous employee interactions.
That trust deficit will not close on its own. It closes when organisations can demonstrate, through a working agentic AI framework, that they know what their agents are doing, that oversight exists, that risk is being monitored, and that governance is not merely aspirational.
Agentic AI Oversight Starts Here
Agentic AI oversight does not begin with a policy document or a governance committee. It begins with knowing what is running inside your enterprise, understanding what it is doing, and having the intelligence to know where the risk is highest.
Portal26’s five-step agentic AI framework gives organisations the structure to build that capability progressively, starting with the three steps that are available now, and extending into enforcement and value as the platform develops.
For many organisations, it will be the first time they have had genuine agentic AI oversight. And given the pace at which autonomous agents are proliferating across enterprise environments, the time to start is now.
Book a demo to see Portal26’s Agent Management Platform (AMP) in action.