The Hidden Risk of Autonomous Agents
AI agents often feel like magic. They act autonomously, seamlessly chaining together data sources, systems, and identities to get work done with little visible effort. From the outside, everything appears simple, it just works. But this sense of magic is exactly where the danger begins. Magic hides the mechanics behind the curtain, obscuring the real complexity underneath. And hidden complexity always hides risk. When actions, permissions, and integrations operate out of sight, organizations lose clarity and control, often without realizing it, until something goes wrong.
A Story About Magic, Shortcuts, and Lost Control
There’s a classic scene In Disney’s The Sorcerer’s Apprentice that many of you may remember, and even if you haven’t seen it, it’s easy to picture:
A young apprentice finds a magical hat and uses it to cast a spell: instead of carrying buckets of water himself, he enchants a broom to do the work for him. At first, it’s a huge success. The broom follows instructions perfectly, hauling water back and forth without complaint. The work gets done fast and effortlessly, and everything feels almost perfect.
Then the apprentice realizes he doesn’t actually know how to stop the spell.
The broom keeps going. More brooms appear. Each one dutifully repeats the task, again and again, until the room is completely flooded. They are doing exactly what the broom was told to do. What’s missing isn’t power or precision, but control.
This scene is a surprisingly accurate way to think about AI agents today. Like the enchanted brooms, AI agents are built to perform tasks for us and ease our workload. But they don’t just follow orders – they autonomously decide how to execute them. This independence is what makes them so effective, but it’s also what makes them unpredictable. Without supervision and control, small decisions can cascade into unexpected outcomes. An agent may be doing exactly what it believes is right, while quietly creating risk in the background. By the time the results are visible, things may already be out of control. The challenge isn’t faulty execution – it’s autonomous execution without enough supervision and control.
The Fine Line Between Autonomy and Chaos
It’s tempting to blame AI autonomy when things go wrong, but autonomy itself isn’t the problem. In fact, it’s the reason AI agents are so valuable in the first place. Giving agents the ability to act independently unlocks speed, scale, and efficiency that humans simply can’t match. Taken on its own, autonomy isn’t the enemy.
The real risk emerges when autonomy operates without clear control. When agents are free to decide how to execute tasks without visibility, boundaries, or oversight, small actions can compound into serious consequences. What starts as helpful efficiency can quietly evolve into runaway behavior, unexpected access, or unintended outcomes.
The Control Gap in Autonomous AI
In a world of autonomous AI, control isn’t about limiting innovation or slowing execution. It’s about creating the oversight needed to supervise powerful agents as they operate independently. A strong control layer ensures AI agents don’t just act quickly, but act in ways that remain visible, understandable, and aligned with intent.
Control starts with visibility: a complete picture of every AI agent, tool, and integration across the environment, including those embedded in SaaS platforms or created outside formal processes. You can’t supervise what you can’t see.
Identity context is where agentic risk begins to emerge. AI agents often operate on behalf of users, services, or systems, invoke APIs calls and use OAuth tokens as they move across tools.
That problem is amplified by the authorization problem: Since AI agents operate autonomously, and not under the identity of the user, their permissions tend to exceed the access permissions the individual users have. While each permission may seem harmless, the aggregated permissions can blur accountability and enable actions that are technically permitted but no longer clearly tied to a single owner or intent. This is how agentic authorization bypass happens in practice, not through a single misconfiguration, but through autonomous execution across fragmented access models.
Without understanding an agent is operating at any moment, and why, oversight quickly breaks down. To address this, control must also include behavior monitoring: continuous supervision of what agents actually do, not just what they’re allowed to do.
Mastering the Magic
Control isn’t about stopping AI’s magic or dialing back its potential. The power of AI agents comes from their ability to act autonomously, move fast, and scale far beyond human limits. But magic without visibility and oversight quickly becomes a liability. Mastery means knowing what agents exist, what they can access, and what they are actually doing as they operate. With the right visibility and supervision in place, organizations don’t have to choose between innovation and safety. They can let AI agents work their magic – confidently, intentionally, and under control.