When AI Has Access: The Hidden Risk in Interconnected Systems
Agentic AI refers to systems that can take action in the real world. These are not just tools that respond to commands. They monitor, decide, and trigger changes across the systems they control. This opens up real opportunities for efficiency, speed, and autonomy in areas like logistics, healthcare, and industrial operations.
Many of the early conversations about agentic AI have focused on oversight, safety, and the potential for systems to misinterpret goals. These are all valid concerns. But there’s another issue that often sits just below the surface. As these systems become more embedded into the infrastructure we rely on, they carry a very different kind of risk.
The Internet Dependency We Didn’t Plan For
To understand what’s at stake, consider our current relationship with The Internet. It was designed as a communications layer, not a dependency. But over time, nearly every essential service has come to rely on it. Transport systems, emergency services, hospitals, energy grids, banking networks, food supply chains. If The Internet were to go offline in any serious way, those systems would immediately feel the impact.
The public wouldn’t just lose websites. They’d lose the ability to pay for things, get fuel, access medication, or contact help. That dependency wasn’t the result of a single decision. It happened gradually, through convenience, cost-cutting, and network effects. And now that it’s here, it’s hard to undo.
Agentic AI Is Heading the Same Way
Agentic AI is starting to follow a similar trajectory. As it gets built into more systems, from grid management and public infrastructure to logistics, healthcare and manufacturing, it will quietly shift from being a helpful tool to a layer of control. And once enough systems depend on it, that control will become a single point of failure.
This creates a new category of vulnerability. If something goes wrong with the AI’s logic, or if it’s manipulated or corrupted, the result won’t just be digital noise. It could interrupt how entire systems function or stop them from working at all.
The more interconnected the system, the less room there is to pull one thread without unravelling the whole thing. It becomes difficult to isolate faults, test changes safely, or even know when a system is misbehaving until something breaks.
The Illusion of Control
A major challenge with embedded agentic systems is that control often becomes abstract. Human operators might assume they are still in charge, but real-world decisions are increasingly being made elsewhere, by systems that are invisible and automated. And if those systems can adjust physical processes without explicit approval, that illusion of control becomes dangerous.
This is especially true when multiple systems are linked. One agentic system managing power distribution might be perfectly safe on its own. But if it’s connected to an AI-controlled transport system, which is in turn connected to automated manufacturing or supply chains, the complexity can quickly outpace human comprehension.
That’s when problems become systemic. A small error in one part of the system might trigger unpredictable behaviour somewhere else, and tracing it back becomes difficult, if not impossible.
Malicious Use Is a Real Possibility
It's also important to recognise the potential for deliberate misuse. If an agentic system with real-world access is compromised, the damage isn’t theoretical. It can affect safety, resources, or public order. And unlike traditional systems that need human input, an agentic system can be triggered to act automatically, across multiple targets, with no further human intervention.
In a worst-case scenario, this could mean manipulation of critical infrastructure by entities who understand the dependencies better than we do.
What Needs to Happen Now
We are not at crisis point, but we are well past the point of needing casual optimism. If agentic AI continues to be embedded in public and private infrastructure, we need to treat it the same way we treat other high-risk systems: with proper oversight, clearly defined limits, and the ability to intervene.
That means:
Avoiding full automation of systems where human context still matters
Ensuring physical systems can still function in degraded modes without AI
Creating clear fallback pathways and emergency controls
Designing AI systems that expose their reasoning in ways humans can understand
Planning for the fact that interconnection creates fragility as well as scale
Agentic AI has enormous potential to help us build better systems. But the more real-world access we give it, the more critical it becomes to ask what happens when it fails, and whether we’ll still be able to act when it does.
