Here is a question worth asking at your next risk committee meeting: how many AI agents are currently running inside your organization, touching your systems, and making decisions on their own? A recent industry survey found the average enterprise is running 37 AI agents right now, and more than half of them operate with no security oversight and no logging at all. Nobody signed off on most of them through any formal process. They got spun up inside a low-code workflow, embedded in a SaaS renewal, or deployed by a team trying to move faster than the last audit cycle allowed.
If that number makes you uneasy, it should. Separate research found that the vast majority of organizations are already using AI agents in some form, while only a small fraction have a clear strategy for actually managing them.
Mostly the same old problem, just faster and harder to see. Fragmented governance processes, meaning risk, compliance, and audit each running their own disconnected tools, have always been the biggest driver of blind spots and unnecessary regulatory exposure. Agentic AI does not invent a new category of risk. It makes the existing one worse.
Think about the difference between old-style shadow IT and what is happening now. An employee pasting a contract into a personal chatbot is one exposure, contained to a single incident. An autonomous agent with standing credentials, connected to your CRM, your document repository, and your financial systems, running continuously and deciding on its own what to touch next, is an entirely different scale of problem. And it is growing fast. Active AI agents inside common enterprise software ecosystems have reportedly grown many times over in the past year alone, far outpacing the governance frameworks most companies built for supervised, single-turn AI tools.
A Risk Management Framework built around spreadsheets and quarterly manual reviews simply was not designed for something that acts continuously. A control tested once a quarter tells you nothing about what an agent did on the days in between.
A few things need to happen together, not in isolation.
Every agent needs to be registered and classified as a distinct risk asset, the same way you would catalog a vendor or a critical application, inside the same Risk Management Framework you already use for everything else. An agent that never made it into that register does not stop acting. It just stops being visible to the people accountable for the outcome.
Third-Party Management deserves particular attention here, since a growing share of these agents are not built in-house at all. They arrive bundled inside vendor tools and SaaS renewals, quietly expanding your exposure through a channel most companies are not watching closely.
Controls also need to update themselves rather than requiring a manual rebuild every time a new AI-specific regulation lands. As frameworks continue to be finalized through 2026, mapping those requirements onto your existing control library and automating evidence collection matters more than adding one more disconnected spreadsheet to track compliance.
Continuous monitoring closes the last gap. Static, point-in-time audits do not work for something that acts every hour of every day. Real-time visibility into whether agents are staying inside policy means an anomaly gets flagged before it becomes an incident, instead of surfacing three weeks later in a quarterly review.
The organizations pulling ahead here are not the ones banning agentic AI outright. They are the ones extending the same connected governance discipline they already trust for people and vendors to the machines now acting on their behalf, before the next agent request comes in rather than after something goes wrong.
Skillmine Complyment brings AI agents, vendors, and human users into one connected Risk Management Framework, so nothing new touching your systems slips through ungoverned.
Empowering organizations to simplify compliance and navigate regulations with confidence.
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