AI agents are moving from pilot projects into everyday infrastructure, and many organizations are losing track of them in the process.
Teams across sales, HR, finance, and operations are spinning up autonomous agents in low-code tools and embedded SaaS features, each one quietly accumulating credentials and permissions that no central function has approved. This pattern is called AI agent sprawl.
Autonomy separates this problem from ones that came before. An unused SaaS subscription may sit dormant; an ungoverned agent keeps acting, accessing data and triggering workflows with nobody watching. And banning employee-built agents repeats the failure of shadow Information Technology (IT) bans a decade ago, pushing the behavior out of view while it keeps happening.
This article breaks down what agent sprawl is, how it takes hold, and the risks it hides. It also lays out a practical governance path, from visibility and human-in-the-loop checkpoints to creation-time controls that keep agents accountable from day one.
AI agent sprawl definition
AI agent sprawl occurs when an organization runs more autonomous agents than it can track or govern with clear ownership. It shows up when teams independently deploy agents, from customer service chatbots to internal workflow automation, without a durable inventory that maps each agent to an owner and access scope. Only 13% of organizations believe they have the right AI agent governance in place, according to Gartner, which suggests most lack confidence in their controls.
Agent sprawl is an even bigger challenge than earlier waves of SaaS and app sprawl. Unlike passive software, agentic systems can execute commands, move production data, modify configurations, and trigger downstream workflows on their own.
A dormant SaaS subscription is mostly an inventory and data-location problem; an ungoverned agent is a live execution path where a mistake can cascade before any human is aware. Detection changes too, because agents often surface as background activity through service accounts or token events, so they rarely appear in the app directories that caught the last decade of sprawl.
Sprawl also isn't limited to unauthorized tools. If unauthorized AI tool use is known as shadow AI, this variation — sanctioned, IT-approved agents behaving in unauthorized or unexpected ways — could be described as "shady AI." These agents feed the problem when teams deploy them without coordination or durable ownership, which is why system-level governance, not one-off approvals, is what actually prevents it.
How sprawl happens even in well-run organizations
Sprawl rarely starts with a bad decision. It starts with a series of reasonable ones. A team automates a workflow, an analyst builds a data agent, a function embeds a SaaS-native assistant, and none of them coordinate.
Multiply those choices across every department, without a central registry or oversight model, and the organization ends up somewhere no one planned. A few patterns show up repeatedly:
Individual decisions look sound in isolation: Teams building agents on their own duplicate similar workflows across the organization, each with its own permissions and owner-specific assumptions.
The barrier to creation has collapsed: AI-assisted coding platforms like Claude Code and Codex make agent creation trivial, while major SaaS platforms now embed agents directly into the tools employees already use.
Stacks are still expanding underneath: Most security and security teams already expect their tech stacks to keep growing, so agents end up landing on environments that are already hard to govern.
Procurement and IT review get skipped: Agents typically arrive as a plugin, a no-code workflow, a SaaS feature toggle, or a quick internal script that quietly gains access to several systems and starts making decisions, bypassing the gates that governed traditional software.
Visibility drops as a result: Most CIOs and CISOs describe limited visibility into how AI is actually being used across the business, with security and IT leaders openly acknowledging that hundreds of service accounts and background agents are running without a clear owner or record of who created them.
The common thread is that governance was designed for a slower cadence of software adoption than the one agents are creating, which is why sprawl now behaves as a cross-functional problem rather than an IT-only one.
Why AI agent sprawl is a cross-functional problem
Agent sprawl stopped being an IT problem the moment business teams could build agents faster than security could track them. The people creating agents and the people owning the risk now sit in different functions, on different timelines. Plus, recent industry research by the Cloud Security Alliance found 65% of organizations experienced at least one cybersecurity incident caused by AI agent use in the prior twelve months.
The pattern is straightforward. Citizen developers across sales, finance, Human Resources (HR), and operations now ship agents in hours using no-code builders, and unauthorized AI use at work has climbed sharply as employees adopt these tools faster than policy can keep up.
Agents built outside IT governance bypass HR and access-request processes, and their accounts often remain active when projects end, invisible to traditional identity and access management (IAM) systems.
Volume then turns oversight from difficult into impractical. As agent counts climb into the thousands per organization, every new agent adds credentials and decision authority to an attack surface that identity governance tools were never built to see.
The security and operational risks of AI agent sprawl
Sprawl doesn't just clutter the environment; it creates specific, compounding risks that show up in security incidents, audits, and data exposure events. Three patterns account for most of the damage:
Duplicate agents solving the same problem inconsistently: When siloed teams build agents unaware of one another, the organization pays for redundant work that a platform-level view would surface for consolidation, with each instance carrying its own credentials, permissions, and assumptions. Isolated agents cannot hand off context or coordinate across domains, which is the opposite of the efficiency that justified building them.
No clear owner when something goes wrong: When an agent takes a damaging action, most teams cannot answer who approved it. JumpCloud research found 55% of organizations lack a centralized way to shut down AI agents quickly, and only 17% assign dedicated accountability for AI agent security. That gap becomes acute under the EU AI Act and ISO 42001, which require auditability and accountability for autonomous systems.
Access and data exposure no one signed off on: Agents inherit broad permissions from user accounts and machine credentials, such as Application Programming Interface (API) keys and Open Authorization (OAuth) connections, accumulate more over time, and rarely get retired, turning a single stolen credential into a large exposure window.
Platforms built for this problem take the opposite approach, giving IT a single view of every agent and workflow running across the business. That visibility makes it possible to spot duplicate agents solving the same problem in different teams, consolidate them under named ownership, and apply consistent scope and guardrails before redundant automations quietly expand the attack surface.
Why banning employee-built agents backfires
Blanket bans feel like the safe move, but they usually remove visibility rather than the behavior. They also cast IT as a blocker to innovation, when the function has always been to enable the business, not stand in its way. By the time most policies land, employees have already developed the habit of turning to AI to close real productivity gaps that sanctioned tools cannot.
That gap is what makes prohibition ineffective. In one study on self-selected workplace AI, 35% of employees said their chosen tool transformed their job, compared with only 18% using employer-chosen platforms.
When the approved path is slow or absent, people route around it, and a ban simply pushes that activity out of sight. Blanket bans increasingly backfire as employees grow more comfortable with generative AI, often switching to personal devices and accounts on networks the organization cannot monitor.
The result is shadow AI that sits entirely outside normal security tooling, alongside shady AI, where approved tools start behaving in unapproved or unexpected ways. That is where the real cost shows up. The more durable approach is a safe, secure environment where people across the business can build agents, apps, and automations, while giving security and IT teams the ability to govern that work.
How to prevent AI agent sprawl: A practical governance framework
Most organizations try to bolt governance onto agents after they're already running in production. That's backward, and it's why sprawl keeps winning. The fix isn't a policy document; it's an environment where governance is architectural, not procedural.
See everything, continuously.
You can't govern what you can't see. Every agent, including shadow ones, needs to be discoverable, with its credentials, connections, and data flows mapped as they actually behave, not as originally approved. Don't wait for a perfect inventory to start enforcing controls on what you can already see.
Let risk decide who's in the loop.
Uniform oversight is a failure mode: it over-restricts safe automation and under-controls the dangerous cases. Reversible, low-impact actions should run autonomously, while irreversible or high-impact ones should require a human with real authority to intervene. Whether that's automatic or human-reviewed shouldn't be a fixed rule. It's a control IT and Security define, so teams can build and act quickly within boundaries that are set centrally, not per-agent.
Make containment structural, not procedural.
The strongest governance isn't a checklist applied after the fact. It's built into how an agent is created and how it runs:
Agents get their own scoped, least-privilege identity instead of inheriting a human's session token
Credentials are revoked automatically when no longer needed
Every action is checked against guardrails before it executes
Ownership and audit trails exist from the moment the agent is created, not added later
When an agent outputs chain into other agents, that same architecture needs to govern the combined behavior, not just each agent individually.
This is the model built for this kind of governance: isolated execution by design, protected credentials, pre-execution policy checks, and full audit trails, so governance isn't something your team maintains on top of AI agents. It's how the environment runs them.
Bring AI agents under governance before sprawl becomes an incident
Agent sprawl is the predictable cost of a capability that outran its controls. Agent creation now happens in low-code and no-code environments, and the procurement and security gates that have governed software for decades do not apply to agents arriving as plugins or via no-code toggles.
The autonomy that makes agents useful is the same property that makes ungoverned agents dangerous, because they keep acting long after anyone remembers they exist.
Teams gives IT and security the control to run and govern employee-built agents, apps, and automations across the business. Organizations can let teams keep using their preferred AI-coding platform, like Claude Code or Codex, and use Tines 3B purely to run and monitor that work, or have their teams build directly in Tines 3B's native builder. Explore how this works by signing up for Tines 3B.
Frequently asked questions
What is the difference between AI agent sprawl and shadow AI?
Shadow AI is unauthorized AI use. Agent sprawl is the broader inventory and governance failure, including approved agents that no one owns. Shadow AI can be one driver of sprawl.
How does the EU AI Act change AI agent governance in 2026?
The EU AI Act's obligations for high-risk AI systems phase in through 2026 and 2027, and autonomous agents making decisions across production systems can fall inside those categories depending on the use case. In-scope agents typically need documented risk assessments, logging and traceability for their actions, meaningful human oversight for high-impact decisions, and a named operator accountable for the system. Combined with ISO/IEC 42001, NIS2 for regulated sectors, and DORA in financial services, the direction of travel is consistent across regimes: named ownership, auditable logs, and proportional human review for autonomous action.
How many AI agents does the average enterprise run?
The honest answer is that most organizations cannot count accurately today, which is the core of the sprawl problem. The operational question is not the headline number but how many of an organization's agents have named owners, documented scopes, and active monitoring today. With confidence in AI agent governance still sitting in the low teens, inventory is almost always outrunning oversight.