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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAI agent sprawl is the uncontrolled growth of AI agents across an organization, to the point where it can no longer reliably discover them, assign owners, manage their permissions, monitor their behavior or retire them. Gartner treats it as a governance and management challenge in its April 2026 guidance. SAP describes the same pattern as agents spreading across systems faster than the enterprise can manage them.
The definition, in plain terms
Having many agents is not the problem. The problem is not knowing what exists. A company with 500 well-registered, owned and reviewed agents is in better shape than one with 50 that nobody can list. Sprawl is a gap in five things:
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- Visibility: no complete inventory of agents.
- Ownership: no named person accountable for each agent.
- Access: unclear identities, permissions and data reach.
- Oversight: no monitoring of what agents actually do.
- Lifecycle: stale, duplicate or abandoned agents never reviewed or shut down.
Okta’s explainer frames the practical test as questions like “How many AI agents do we currently have deployed?” If nobody can answer that with confidence, sprawl is already present.
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Why it matters more than app sprawl
Agents can access data, call tools and start business processes. An error can therefore go beyond a wrong answer to an action in a connected system, as SAP and Microsoft’s security guidance both note. Gartner analyst Max Goss says ungoverned sprawl exposes organizations to risks “including misinformation, oversharing and data loss.”
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Agent sprawl vs. shadow AI
The terms overlap but are not the same. Sprawl is the inventory and governance gap. Shadow AI means agents or AI tools running without proper security oversight. According to Okta, losing visibility makes shadow AI harder to prevent. Gartner’s advice covers both: the inventory should include sanctioned and shadow agents.
How big is the problem?
| Figure | Source and caveat |
|---|---|
| Over 150,000 agents by 2028, up from fewer than 15 in 2025, for the average global Fortune 500 enterprise | Gartner’s 2026 prediction, not an observed count. Gartner |
| 13% of organizations think they have the right AI agent governance in place | Gartner, 2026. Same release. |
| 98% of surveyed companies have deployed agents or plan to | SAP LeanIX survey as reported by SAP News Center; methodology not given in the article. |
| Fewer than half of surveyed organizations have visibility into an inventory of agents | Same SAP-reported survey and same limitation. |
The SAP figures come from a vendor with a product in this space, so treat them as indicative rather than independently verified.
How to control it
Gartner’s six steps are a workable backbone.
1. Set creation and connector policies
Define who may build and share agents and which connectors are allowed.
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2. Build a central inventory
Register every agent, sanctioned or informal. Record purpose, owner, identity, permissions, data access, risk and operational status (Microsoft).
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3. Define identity, access and retirement
Give agents their own identities with least-privilege permissions, periodic recertification, and decommissioning of unused or stale agents. Microsoft also recommends supervising agent-to-agent communication and keeping memory and retrieval stores clean where agents share persistent context.
4. Govern the data agents can reach
Limit data exposure, since oversharing is one of the named risks.
5. Monitor and remediate
Baseline normal behavior, alert on anomalies and act on agents operating outside their intended scope.
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Teach employees the rules and share good practices. Gartner’s Goss says organizations must “govern agents and manage sprawl, but also safely empower employees to innovate.”
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Before deployment, Microsoft suggests writing an agent charter covering responsibilities, business objectives, role boundaries and prohibited actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Operating model for large organizations
AWS’s July 2026 guidance proposes hub-and-spoke governance. A central council sets minimum standards and keeps the shared registry. Each business unit names a governance lead and builds within those guardrails. AWS says strict regulation can justify a more centralized model. Its key principle: “The primary objective of the central team is to make the governed path faster than the ungoverned workaround.” This is vendor guidance, not independent evidence, but the logic is sound. If approval is slow, teams will go around it.
Quick Recap
What to compare when choosing governance tools or approaches
- Centralized versus federated control.
- Breadth and freshness of agent discovery.
- How tightly identity and permissions are scoped.
- Whether monitoring covers agent actions and interactions.
- Lifecycle support from creation to retirement.
- Interoperability across vendors.
- Whether the approved route is easy enough that teams use it.
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