Agent sprawl: the new shadow IT

AI agents usually do not enter an organization through a big strategic programme. They start smaller.

An employee creates an agent that summarizes emails. A team builds a workflow that prepares proposals. Someone connects a chatbot to internal documents. A department experiments with automated reporting. It works, it saves time, and it feels logical to continue. Until nobody knows exactly how many agents are actually running.

That is where agent sprawl begins: a growing landscape of AI agents, scripts, workflows and integrations that started out useful, but slowly move out of the organization’s field of view. And that makes it look a lot like something we already know: shadow IT.

From useful shortcut to invisible risk

Shadow IT emerged because teams wanted to move faster than central IT could deliver. They started using their own tools, spreadsheets, cloud apps and automations. Often with good intentions. Often with real productivity gains.

But over time, questions appeared.

• Who has access?
• Where is the data stored?
• Which version is the source of truth?
• What happens when someone leaves?
• Who steps in when something goes wrong?

AI agents add a new layer of questions. Which actions is an agent allowed to perform on its own?
Which sources does it use to make or prepare decisions? Which prompts, instructions and permissions shape its behaviour? Who owns the output? How do we check whether it still does what it was meant to do?

A regular SaaS tool usually stores information or supports a process. An AI agent can also act. That changes the impact.

Why agent sprawl happens so quickly

AI agents are attractive because they sit close to the work. They do not solve an abstract IT problem. They solve a concrete daily problem.

• A sales colleague wants to gather customer information faster.
• A recruiter wants CVs summarized.
• A consultant wants notes turned into actions.
• A manager wants automatic updates from multiple systems.

These use cases feel small and practical. So the risk also feels small. But agents are rarely used in isolation. They get access to documents, mailboxes, CRM systems, project tools or databases. They are connected to other workflows. They use prompts that start to behave almost like policy rules. And they produce output that people begin to trust.

Before long, the organization no longer has one AI initiative. It has dozens of small AI initiatives running next to each other. Not necessarily wrong. Vulnerable, though, if nobody sees the whole picture.

The problem is not autonomy. It is ambiguity.

AI agents do not all need to be locked down centrally. That would remove much of their value. Their strength lies in speed, proximity and experimentation.

The problem starts when autonomy is not connected to ownership.

• An agent without an owner is a risk.
• An agent without access rules is a risk.
• An agent without logging is a risk.
• An agent without evaluation is a risk.
• An agent that was once useful, but nobody really understands anymore, is definitely a risk.

So the question is not: “Should teams be allowed to use AI agents?” The better question is: “How do we make sure teams can use AI agents without losing overview, safety and responsibility?”

What organizations need

Agent governance does not have to start heavy or bureaucratic. In fact, if it becomes too heavy, people will work around it. That is exactly how you recreate the shadow problem you were trying to prevent.

A practical foundation starts with a few simple agreements.

1. Make agents visible: Start with a register. Which agents exist? What are they used for? Which systems do they touch? Who owns them?

2. Define ownership: Every agent needs a functional owner. Not just a technical contact, but someone responsible for its purpose, behaviour and risks.

3. Limit access deliberately: An agent should not receive more permissions than it needs. Especially not broad access to mailboxes, drives or customer data “just to make things easier”.

4. Log important actions: If an agent retrieves information, prepares decisions or performs actions, you want to be able to see what happened afterwards.

5. Review periodically: Agents change along with processes, data and teams. What is valid today may be outdated three months from now.

6. Make stopping normal: Not every agent has to keep existing. Temporary experiments may remain temporary. Cleaning up is part of mature use.

The role of IT changes

With classic shadow IT, IT was often seen as the brake. With AI agents, that will not work. The need to experiment is too strong and the technology is moving too fast.

IT, security and management therefore need to be more than gatekeepers. They need to design safe room to move. That means clear guardrails, reusable building blocks and low-threshold support. Teams should know what is allowed, what is not, and when to ask for help. That is how governance stops feeling like control after the fact. It becomes part of the design from the start.

Start small, but start consciously

Most organizations do not need a complete agent governance framework tomorrow. But waiting until the landscape has become unclear will make everything harder. Start with the agents that already exist. Map them. Look at access, ownership and impact. Decide which use cases are low risk and which ones need more attention. Grow from there.

Because agent sprawl does not happen because people are being irresponsible. It happens because good ideas grow faster than the agreements around them. That is the opportunity. Organizations that learn now how to use AI agents in a clear, safe and practical way are not just creating more control. They are building trust. And trust is exactly what is needed for AI to move beyond experimentation and become part of real work.

Start small, but keep overview.
If you want to start working with AI agents, make sure ownership, access and control are part of the design from the beginning. Orbis Neo can help you make that practical.