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| AI Agent Sprawl Explained: What It Is, Risks, and How Businesses Can Manage AI Agent |
AI agent sprawl is happening fast in companies today. A sales team creates an AI agent to write follow-up emails. A finance team builds another to match invoices. A support team uses one to answer customer questions. Each agent gets built quickly works well and fixes a problem.. Six months later no one in IT knows how many AI agents are running across the company. They don’t know what data the agents can access.. No one is sure who would be responsible if one of them makes a big mistake. That is AI agent sprawl. It is one of the growing problems in modern businesses.
This article explains what AI agent sprawl really means. It looks at why it's spreading so quickly. It details the risks it brings. Like security issues, data leaks and costly errors.. It gives practical steps companies can take to stop the spread and keep their AI agents under control before things spiral, out of hand.
What Is AI Agent Sprawl
AI agent sprawl is the growth of AI agents inside a company. It happens when different teams create or bring in AI agents without anyone having a view of what is going on. There is no control, no clear ownership and no proper rules to manage these agents. This kind of sprawl is not about having many agents. It's about losing track of them.
An AI agent is not the same as a chatbot. A chatbot mostly answers questions. An AI agent can do work. It can get data from a database send an email update a file or start another program.. It can often do these things on its own with little or no approval from a person each time. That makes them powerful—but risky if not managed.
Sprawl happens when departments act on their own. One team picks a tool from a vendor. Another builds something using a low-code platform. A third connects an AI assistant directly to a shared folder. Each of these actions seems harmless at first.. Over time the company ends up with a large number of AI agents. These agents may have access to data. They may have levels of permission.. In many cases no one knows who is responsible for them.
IBM explains this well. They say sprawl occurs when speed wins over visibility. Teams rush to use AI agents to automate tasks.. They do not follow a shared plan. There is no coordination across the organization. As a result the number of AI agents grows quickly. So does the risk.
Gartner has a number for the future. They say that by 2028 the average global Fortune 500 company will be running than 150,000 AI agents. That is up from than 15 in 2025. That is not a mistake. It shows how the use of AI agents is expected to grow. Once the tools become easy enough even people, without coding skills can build agents. That means the growth will not slow down. It will accelerate.. Without proper governance the risks will grow too.
Why AI Agent Sprawl Is Growing So Fast
A few things are driving this growth at the same time.
Agent building tools are now very easy to use. Many platforms allow non‑technical employees to create an AI agent with a few clicks connect the AI agent to company software and start using the AI agent the same day. This ease of use boosts productivity. It also means that AI agents are created without the careful review that a normal software project would normally require.
Departments solve their problems. Teams do not wait for a company‑wide AI strategy when a task needs solving now. A single AI agent that saves a hours a week seems like an easy win and most employees do not think about how that AI agent fits into a larger governance picture.
There is pressure to show AI results. Many organizations want proof that they are using AI effectively and shipping a working AI agent quickly is one of the easiest ways to demonstrate that. This pressure can push teams to choose speed over documentation and oversight.
AI agents can create AI agents. Some AI systems can now spin up AI agents or subtasks on their own to finish a job. Research, on enterprise AI agent deployments shows that about a quarter of AI agents can autonomously create and assign AI agents. When this happens without a human approving each AI agent the number of active AI agents can grow far faster than any team can track manually.
The Real Risks of AI Agent Sprawl
An Expanding Attack Surface
Every AI assistant that links to an API, a database or another type of software creates a possible way for bad people to try to break in. Recos study about agent sprawl shows that every new assistant brings connections, to cloud apps, APIs and data sources which means more chances for someone to find a weakness. Unlike a system that is clearly known growing assistants are usually not listed anywhere so security teams cannot fix, watch or control something they are not even aware of.
Oversharing and Data Exposure
Agents often need access to perform their jobs well for example reading customer records or internal documents. If permission design is not careful an agent created for one task can end up with far more access than the agent actually needs. Gartner analyst Max Goss has warned that ungoverned agent sprawl puts organizations at risk including misinformation, oversharing and data loss. When an agents access is not scoped tightly a single compromised or misconfigured agent can expose data, beyond the agents intended purpose.
Weak Identity and Access Controls
Research from OutSystems shows that around 22% of organizations treat AI agents as separate entities with their own limited access rights. Meanwhile 45.6% still use API keys when connecting agents to each other. Using shared credentials makes it very hard to tell which agent carried out an action especially when trying to investigate a security incident. The same study found that over half of the AI agents that are deployed do not have any security monitoring, in place. It also found that many of them lack logging.
Duplicate Work and Conflicting Outputs
Sprawl is not a security issue. Sprawl is also an issue. I see that Salesforces 2026 Connectivity Benchmark report found that the average organization already uses twelve or more AI agents but half of AI agents operate in silos than, as part of a coordinated system. When teams build the kind of AI agent independently they often end up maintaining separate versions of the same data pipeline. This can produce answers or duplicate effort across the business.
Slower, Harder Incident Response
When something goes wrong like an agent making a mistake or sharing information the first thing people always ask is who is in charge of that agent and what it can see. In a setup figuring out the answer, to that question can take hours or even days and sometimes there is no answer at all. IBM points out that having many agents around makes it much harder to fix problems quickly because its not always easy to find the right team to handle the issue.
Regulatory and Compliance Exposure
Regulatory attention on AI is increasing, and agents that touch personal data, financial information, or other regulated content raise the stakes further. In the European Union, the AI Act reached full enforcement on August 2, 2026, which means organizations need to know which of their deployed agents fall under high-risk categories and whether the required conformity assessments have been completed. A company that cannot even produce a full list of its active AI agents will struggle to meet that kind of regulatory requirement.
How Businesses Can Manage AI Agent Sprawl
The goal is not to shut down every agent a team has built. Gartner's Max Goss has pointed out that many organizations respond to sprawl by blocking or restricting agent use, but this usually backfires, since employees who cannot use approved tools will often turn to unsanctioned ones instead, which creates even greater risk. A more realistic approach combines clear governance with room for teams to keep innovating safely. Gartner has outlined six practical steps that map well onto this goal.
1. Make Sure There Are Clear Rules and Guidelines
Create rules about how agents are made who is allowed to make them and which systems they can work with. This doesn't have to be a legal paper. It has to be clear enough that a team making an agent knows exactly what to do before it starts working.
2. Create a Main List of All Agents
You can't control something if you don't know it exists. Companies should make a list of every agent being used including any made inside the company and any bought from outside. Many companies use AI trust, risk and security management tools to find and organize agents across the business those working outside official ways.
3. Set Up Identity, Access and Life Cycle Rules
Each agent should have its identity instead of using the same passwords as other systems or a persons login. This helps track what the agent did if something goes wrong. Companies also need a plan for checking and removing agents that are no longer needed because inactive or forgotten agents create a lot of risk.
4. Make Sure Data Access is Controlled
Decide what data each agent can see and keep that access up to date. This includes checking permissions often making sure an agent doesn't have access than it needs and taking away access to data that is no longer important for the agent to use.
5. Keep Watching Agents and Fix Problems as They Happen
Agent rules are not something you do once and forget. Companies need to keep checking how agents are acting, including spotting behavior and fixing agents that go beyond what they were made for. Since than half of agents in some surveys work, without any logs adding basic watching is often one of the most important steps a company can take early.
6. Encourage People to Use AI the Right Way
rules only work if people understand why they are important. Training and sharing practices help teams making agents see the risks instead of thinking rules are just something that gets in the way of their work.
AI Agent Sprawl vs. Shadow AI: How They're Connected
AI agent sprawl and shadow AI are often talked about together. There is a strong reason for that. Shadow AI happens when workers use AI tools that IT has not given permission for. Agent sprawl usually comes from the kind of behavior but on a much bigger level. An employee who uses a chatbot is a shadow AI issue. An employee who creates an AI agent that can access a shared drive send emails and update a CRM record by itself is a much bigger issue because that agent can take real actions without anyone checking each step.
This is part of why Gartner warns against dealing with sprawl by using blanket bans. When you block approved agent-building tools without giving a replacement it often forces the same kind of behavior into unofficial channels, where it is even harder to spot and manage. A better approach sees agent sprawl. Shadow AI, as two parts of the same governance problem instead of two separate issues that need separate solutions.
Questions Businesses Often Ask
How many AI agents does a typical company actually have? More than most leadership teams expect. Salesforce's 2026 research found the average organization already runs 12 or more agents, and Gartner's longer-term projection puts the number in the tens of thousands per large enterprise within a few years. Even smaller businesses are often surprised by how many agents show up once they run a proper inventory, since agents get built inside marketing platforms, CRMs, and other everyday software, not just as standalone projects.
Is agent sprawl mainly an IT problem or a business problem? It is both. IT and security teams need to build the inventory, identity, and monitoring systems that make agents visible and traceable. But the agents themselves are usually created by business teams solving their own problems, so any governance plan that does not involve those teams directly will struggle to keep up with how fast new agents get built.
Do small and mid-sized businesses need to worry about this too? Yes. A smaller business may have fewer agents overall, but it typically has less dedicated security staff watching for sprawl and fewer formal approval processes. A handful of ungoverned agents with broad access to customer data can create serious exposure even without enterprise-level scale.
The Bottom Line
AI agent sprawl is not a future risk. It is already happening inside most companies that have let individual teams adopt AI agents on their own timeline. The businesses that will manage this well are not the ones trying to ban agents outright, since that approach tends to push the same behavior underground as shadow AI. The businesses that succeed are the ones building an accurate inventory, assigning clear ownership, and putting real monitoring in place before an incident forces the issue. Agent sprawl grows quietly, one convenient shortcut at a time, which is exactly why it needs a deliberate, organization-wide response rather than a wait-and-see approach.
Disclaimer: AI governance frameworks, regulations, and vendor tools in this space are changing quickly. Businesses should confirm current requirements, including regional regulations such as the EU AI Act, with official sources and qualified legal or compliance professionals before finalizing their own AI agent governance program.
Sources:
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NIST — AI Risk Management Framework
NIST AI Risk Management Framework -
OWASP — Agentic AI Security
OWASP GenAI Security Project
