Summary
AI agents are moving quickly from experimental tools to everyday enterprise systems. They can summarize information, automate workflows, interact with software, connect to company data, and support employees across departments.
But as adoption increases, a new problem is emerging: AI agent sprawl.
Gartner predicts that by 2028, the average Global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. The firm also says only 13% of organizations believe they currently have the right AI agent governance in place.
For enterprise technology leaders, this is a serious warning. AI agents may improve productivity, but without governance they can create risk across data security, identity management, compliance, misinformation, and operational control.
AI Agents Are Scaling Faster Than Governance
The enterprise AI conversation has moved beyond chatbots. Companies are now experimenting with agents that can act across tools, retrieve information, trigger workflows, create content, assist customer support teams, support sales operations, and help employees complete repetitive tasks.
That makes AI agents powerful. It also makes them difficult to control.
A single chatbot may be easy to monitor. Thousands of agents across departments, business units, SaaS platforms, and employee-built workflows are a very different challenge. Each agent may have access to different data, different permissions, different tools, and different business processes.
Gartner’s warning is based on this shift. The firm says AI agent sprawl can create IT complexity and management challenges as organizations move from limited pilots to broad enterprise use.
The issue is not only how many agents exist. The larger issue is whether companies know what those agents are doing, who created them, what data they can access, and whether they are still needed.
What Is AI Agent Sprawl?
AI agent sprawl happens when an organization has too many AI agents operating without enough visibility, ownership, governance, or lifecycle control.
This can happen in several ways.
A marketing team may create agents for campaign research and content workflows. A sales team may create agents for lead summaries and account intelligence. A support team may create agents to respond to customer questions. Developers may create coding agents. Operations teams may use agents for reporting, analysis, and internal automation.
Each of these use cases may be useful on its own. But when hundreds or thousands of agents are created without a central inventory, the company may lose control.
Some agents may become outdated. Some may have excessive permissions. Some may use old or incorrect data. Some may expose sensitive information. Others may continue running even after the employee or team that created them has moved on.
This is similar to earlier enterprise problems such as SaaS sprawl, shadow IT, unmanaged APIs, and untracked automation scripts. The difference is that AI agents may not only store or move data. They may also interpret information, generate outputs, make recommendations, and take actions.
Why Blocking AI Agents Is Not a Long-Term Answer
One natural response to AI risk is restriction. Companies may block certain tools or prevent employees from creating agents.
Gartner argues that this is not a sustainable long-term solution. If employees cannot use approved tools, they may move to unsanctioned tools and create a bigger shadow AI problem.
This is an important point for CIOs and security leaders.
Employees usually adopt new tools because they solve real workflow problems. If the approved enterprise environment is too slow, too limited, or too restrictive, teams may find their own alternatives. That can push sensitive work into consumer AI tools, unmanaged SaaS products, or personal accounts.
The better strategy is not to block everything. It is to create safe pathways for adoption.
Companies need approved tools, clear rules, monitored usage, and practical training. The goal should be to enable innovation while reducing uncontrolled risk.
The Six Governance Steps Gartner Recommends
Gartner identified six steps to help organizations manage AI agent sprawl. These steps provide a useful framework for enterprise AI governance.
1. Establish agent governance and policies
Organizations need clear rules for when agents can be built, who can create them, how they can be shared, and which connectors are allowed. This matters because many agents become risky when they connect to sensitive systems without proper controls.
A company should define different levels of risk. A low-risk internal summarization agent is not the same as an agent that can access customer records, financial data, or production systems.
2. Build a centralized agent inventory
Enterprises need to know which agents exist across sanctioned and unsanctioned environments. Gartner says organizations can use AI trust, risk, and security management tools to discover and categorize agents across applications.
This inventory should track ownership, purpose, data access, integrations, risk level, usage, and lifecycle status. Without an inventory, governance becomes guesswork.
3. Define agent identity, permissions, and lifecycle models
AI agents need identity and access management discipline. Companies should define what each agent is allowed to access, what actions it can take, who owns it, and when it should be reviewed or retired.
This is especially important when agents connect to business applications. A poorly managed agent could overshare confidential information, access systems beyond its role, or continue operating after its purpose has expired.
4. Develop AI information governance
Agents are only as safe as the information they can reach. Companies need to control what data agents can access, how permissions are managed, how information stays current, and when obsolete data should be archived.
This is critical for regulated industries such as finance, healthcare, insurance, legal services, and government. But it also matters for any company handling customer data, intellectual property, employee records, or internal strategy documents.
5. Monitor and remediate agent behavior
AI governance cannot stop after deployment. Organizations need ongoing visibility into agent activity. They must monitor whether agents comply with policy, detect unusual behavior, and correct agents that exceed their intended purpose or risk tolerance.
This is where AI governance becomes operational. Enterprises need logs, alerts, approval workflows, exception handling, and remediation processes.
6. Build a culture of responsible AI usage
Technology controls alone are not enough. Employees need training, best practices, and communities of practice so they understand how to use agents responsibly.
This matters because many AI risks come from ordinary user behavior: uploading sensitive files, connecting the wrong data source, trusting outputs without review, or sharing agent-generated results without verification.
Responsible AI has to become part of the workplace culture, not only a compliance document.
Why Agent Identity Will Become a Major Enterprise Issue
One of the most important ideas in Gartner’s framework is agent identity.
In traditional IT, users and applications have identities. They are given permissions, roles, and access controls. AI agents will need similar treatment.
If an AI agent can retrieve data, summarize documents, send messages, create tickets, update records, or trigger workflows, it should not operate as an invisible system. It needs a defined identity, an owner, permissions, monitoring, and accountability.
This will likely become a major focus for identity and access management vendors, cybersecurity teams, and enterprise architecture leaders.
In the future, companies may need to answer questions such as:
Who created this agent?
What business function does it support?
What data can it access?
Can it take actions or only provide recommendations?
When was it last reviewed?
Who is responsible if it makes a mistake?
Should it still exist?
Without answers to these questions, AI agents could become a new layer of unmanaged enterprise risk.
The Data Access Problem
AI agents are especially risky when they connect to internal knowledge systems.
Many enterprise AI tools become useful because they can access documents, messages, CRM records, tickets, code repositories, databases, or collaboration platforms. But this also increases the chance of oversharing.
An agent may summarize a confidential document for someone who should not see it. It may retrieve outdated policy information. It may combine data from different systems in ways the company did not intend. It may expose sensitive information through a generated answer.
This is why AI information governance is now becoming central to enterprise AI strategy.
Companies need clean permission structures, reliable data classification, updated access policies, and strong retrieval controls. Otherwise, AI agents may amplify existing data governance weaknesses.
Why This Matters for CIOs, CISOs, and Business Leaders
AI agent sprawl is not only an IT problem. It affects business performance, security, compliance, and employee productivity.
For CIOs, the challenge is to enable AI adoption without creating an unmanageable technology layer.
For CISOs, the priority is to reduce data leakage, unauthorized access, shadow AI usage, and risky integrations.
For legal and compliance teams, AI agents raise questions around auditability, data handling, decision support, and regulatory exposure.
For business leaders, the risk is operational confusion. If agents generate inaccurate recommendations or act outside approved workflows, they can affect customers, employees, and business decisions.
This is why governance must be designed early. Waiting until agents are already spread across the organization will make the problem harder and more expensive to fix.
TechInsyte Take
AI agents may become one of the most important enterprise technologies of the next few years. But the same features that make them useful also make them risky.
They can connect to systems, retrieve data, automate tasks, and support employees. But if organizations do not manage identity, permissions, data access, monitoring, and lifecycle control, agent adoption can turn into agent sprawl.
The winning companies will not be the ones that block AI agents completely. They will be the ones that create controlled environments where employees can use agents safely, productively, and responsibly.
For TechInsyte readers, the message is clear: AI agent governance is no longer a future issue. It is becoming a core enterprise architecture requirement.
FAQs
What is AI agent sprawl?
AI agent sprawl refers to the uncontrolled growth of AI agents across an organization without enough visibility, ownership, governance, access control, or lifecycle management.
Why is AI agent sprawl risky?
It can create risks around misinformation, data leakage, oversharing, shadow AI, excessive permissions, compliance exposure, and unmanaged automation.
What did Gartner predict about AI agents?
Gartner predicts that by 2028, the average Global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025.
Source link: Gartner
