What is AI agent sprawl?
By Identra · Updated
AI agent sprawl is the unmanaged growth of AI agents and their associated access across an organization. It occurs when teams can no longer reliably identify which agents exist, who owns them, what they can do, or when their access should end.
Key numbers
- Machine identities outnumber human identities 82 to 1 in the average organization (CyberArk Identity Security Landscape, 2025)
- 33% of enterprise software applications will include agentic AI by 2028, up from under 1% in 2024 (Gartner press release, June 2025)
- Over 40% of agentic AI projects will be canceled by end of 2027, often for inadequate risk controls (Gartner press release, June 2025)
What causes AI agent sprawl?
Agents can enter an enterprise through SaaS features, coding tools, browser automation, internal applications, and experiments. Teams may approve the host application without separately reviewing the agents created inside it. Copies, test deployments, scheduled tasks, and delegated work can then drift beyond the original approval.
The problem is a gap between deployment and accountability. An agent does not need its own account to contribute to sprawl. It may use a shared service account, an employee session, or credentials supplied by its platform. Tracking an AI agent identity therefore requires connecting the agent to its actual access, even when that access belongs to another identity.
A large, well-managed agent estate is not inherently sprawl. The warning sign is an inability to explain an agent's purpose, authority, owner, or retirement process.
Why is AI agent sprawl a security risk?
Untracked agents make access decisions harder to review. An experiment may retain production permissions after testing ends. A copied agent may inherit a connector intended for a different task. Shared credentials can make it difficult to distinguish authorized work from misuse.
Agents that read untrusted content and invoke tools also need protection against prompt injection. Sprawl does not cause that vulnerability, but it makes finding affected deployments, updating controls, and investigating actions harder.
Stopping an agent does not necessarily remove its access. API keys, refresh tokens, connector grants, and scheduled triggers may remain usable. Effective retirement must address both the running workload and the authority behind it. Otherwise, a paused project can leave an active access path without an accountable owner.
What does AI agent sprawl look like in an enterprise?
Consider a hypothetical support team that builds an agent to summarize customer tickets. It connects the agent to a ticketing system and a shared document repository. Another team copies the configuration for account research, while a developer runs a local version to troubleshoot failures.
The pilot ends, but the copied agent remains scheduled and the local configuration still contains a valid credential. The original owner changes roles. Security knows the ticketing application is approved, yet cannot readily identify which agent is reading customer records or why it still needs access.
A useful response is to identify the deployments, confirm their owners, pause unnecessary jobs, and remove obsolete grants. Before revoking a shared credential, identify legitimate dependencies and move them to separately managed access. Deleting the original agent alone would leave the other deployments and permissions unresolved.
How is agent sprawl different from shadow AI?
These problems overlap, but they describe different management gaps. Shadow AI concerns AI use outside organizational approval or oversight. Agent sprawl can develop entirely within approved platforms when agents and their access stop receiving adequate lifecycle management.
Shadow AI
- What is unmanaged?
- AI use outside approval or oversight
- Example
- An employee connects an unapproved AI service to work data.
AI agent sprawl
- What is unmanaged?
- Agent deployments, ownership, and authority
- Example
- An approved platform contains abandoned agents with active connectors.
Secret sprawl
- What is unmanaged?
- Credentials spread across locations and systems
- Example
- An agent's API key is copied into scripts and local configuration files.
Identity sprawl
- What is unmanaged?
- Accounts, identities, and access relationships
- Example
- Unused service accounts remain after projects end.
How do you discover and inventory AI agents?
Start with agent platform inventories, SaaS administration records, endpoint software inventories, browser automation deployments, and application owners. Cross-check these against service accounts, OAuth grants, scheduled jobs, and access logs. Model API traffic can guide investigation, but it does not prove that an agent exists or identify its owner.
Maintain an agent registry that connects each managed agent definition to its deployments and access. Distinguish a reusable agent from an individual run so temporary execution does not create misleading inventory entries.
- Record the business purpose, accountable owner, deployment location, and approval status.
- Map credentials, delegated user access, connected tools, reachable data, and permitted actions.
- Record relevant versions and parent or delegated agents where applicable.
- Set a review date or expiry condition, and document how to stop execution and revoke access.
- Reconcile the registry with observed activity and platform records when deployments, permissions, or owners change.
How do you prevent and clean up AI agent sprawl?
Make ownership and access review part of deployment. Apply least privilege to the tools, data, and actions required for the approved task. OWASP's authorization guidance supports limiting permissions and reviewing them for privilege creep.
Build retirement into the non-human identity lifecycle. Good control means the team can explain an agent's current authority, investigate its actions, and stop both its execution and future access.
- Prioritize unknown owners, sensitive data access, administrative permissions, and agents that can send messages or change production systems.
- Separate test and production access. Avoid shared credentials where practical, and prefer short-lived credentials where supported.
- Review new connectors, broader permissions, and delegation changes before enabling them.
- Require human approval for consequential actions, with the proposed target and operation visible to the reviewer.
- Keep action records that connect the agent, run, acting identity, tool, target, and outcome without unnecessarily retaining sensitive content.
- Test shutdown and revocation procedures. During retirement, disable triggers, remove grants, invalidate credentials, and verify that access has ended.
How Identra thinks about it
Identra helps teams find agents that have spread beyond review. It discovers AI agents across Microsoft 365 Copilot and Foundry, Google Workspace, and Anthropic with their owners, and on managed macOS and Windows devices it finds coding agents, MCP servers, plugins, and agent tasks. Teams can then apply policy to agent tool calls on endpoints and to AI agents driving the browser.
Go deeper: AI discovery across identity, SaaS and cloud
Frequently asked questions
Does every AI agent need a separate identity?
No. Some agents act through platform identities or delegated user access. Each agent still needs an accountable owner and a traceable relationship to the permissions it uses.
Can approved AI platforms still have agent sprawl?
Yes. Platform approval does not establish ownership, appropriate permissions, or retirement rules for every agent created within it.
What is an orphaned AI agent?
An orphaned AI agent has no current accountable owner. It may still run, or its deployment may be inactive while associated credentials and grants remain usable.
Should temporary agent runs each have a registry entry?
Usually, track the managed agent definition and deployments in the registry, then connect temporary runs through execution records. Separately track delegated agents when they introduce distinct ownership, access, or lifecycle requirements.
Is deleting an agent enough to retire it?
No. Check for remaining schedules, copies, sessions, credentials, and connector grants. Verify that retired access no longer works, while accounting for legitimate workloads that shared it.