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AI agents are rapidly evolving from productivity assistants into active participants in enterprise operations, automating workflows, making real-time decisions, and connecting systems across the entire organization. That evolution is creating enormous value. It is also creating a problem most enterprises did not see coming.
When organizations begin deploying AI agents, they rarely start with a governance plan. They start with a use case: a team automates a workflow, an analyst builds a data agent, an engineer ships an internal tool. Each decision is reasonable in isolation. But multiply those decisions across every department, every quarter, without a central registry or oversight model, and you arrive somewhere no one intended: AI agent sprawl.
Agent sprawl is the uncontrolled proliferation of AI agents across an organization without centralized visibility, governance, or ownership. And here is the part most enterprise leaders find counterintuitive:
"Organizations don't intentionally create agent sprawl. Interestingly, it is an after-effect of successful AI adoption in the company, which also drives governance challenges." - Gartner,
That framing changes everything. Sprawl is not a sign that AI adoption has failed. It is a sign that it has succeeded faster than the governance infrastructure around it could keep up. The challenge now is not to slow down adoption, but to build the systems that let it continue safely and at scale.
The stakes are significant. Gartner predicts that by 2028, the average Fortune 500 enterprise will have over 150,000 AI agents in use, up from fewer than 15 in 2025. Yet only 13% of organizations believe they have the right governance in place today. That 87% gap represents real exposure: security blindspots, compliance risk, redundant costs, and ungoverned systems making decisions on behalf of the business.
This guide covers everything enterprise leaders need to know: what agent sprawl actually is and how it starts, why it happens even in well-governed organizations, the security and compliance risks it creates, how to detect it, and the six-step framework Gartner recommends to get it under control. It also introduces the emerging platform category built specifically to solve it at scale, the AI Agent Management Platform, and the role Kore.ai plays within it.
What is AI agent sprawl? A complete definition
AI agent sprawl is the uncontrolled growth of AI agents across an organization without centralized visibility, governance, or accountability. It occurs when departments, business units, and application teams deploy agents independently to automate tasks, access data, or orchestrate workflows, without consistent security controls, ownership models, or lifecycle management processes.
Unlike traditional software deployments that go through procurement and IT review, AI agents can be built and shipped in hours. A marketing team spins up a campaign agent. A finance team deploys a reconciliation assistant. An engineering team ships an internal automation tool. None of these decisions is reckless. But without a central registry, a shared governance model, or defined ownership, each one adds to a growing ecosystem that nobody has a full map of.
Over time, organizations lose the ability to answer the most basic governance questions:
- How many agents are running across the organization?
- What systems, data, and APIs can they access?
- Who owns them, and who is accountable when something goes wrong?
- What business purpose do they serve, and are they still serving it?
When those questions cannot be answered confidently, agent sprawl has already begun.
The difference between agent sprawl and the SaaS sprawl enterprises dealt with in the previous decade is not just scale. It is an agency. A forgotten SaaS subscription sits idle. An ungoverned AI agent continues to act: accessing data, triggering workflows, connecting to downstream systems, and making decisions, often without any human awareness it is still running.
How fast is AI agent adoption growing? The numbers every CIO needs to see
The pace of adoption is without precedent in enterprise technology. Fewer than 15 agents in 2025 to over 150,000 by 2028 represents a 10,000x increase in three years. What makes this particularly challenging for IT and security leaders is that 87% of large enterprises, by their own admission, are operating without adequate controls over systems that can take real actions on their behalf.
What causes AI agent sprawl in enterprises
Organizations don't intentionally create agent sprawl; interestingly, it is an after-effect of successful AI adoption in the company, which also drives governance challenges. These six forces make ungoverned proliferation the default outcome in any organization without deliberate counter-pressure:
AI agent sprawl vs. shadow AI: what's the difference
These two terms are frequently confused or used interchangeably. Understanding the distinction, and crucially the direction of causality between them, is essential for designing an effective governance strategy.
Many organizations resort to blocking or restricting the use of AI agents, but this is not a long-term solution. If employees are unable to work in the sanctioned tools, they will likely go around the organization's controls and start using shadow AI, which presents far greater risks.
Max Goss, Sr. Director Analyst, Gartner - Digital Workplace Summit, London, April 2026
Security and compliance risks of AI agent sprawl
After more than 12 years of deploying AI in some of the world's most regulated industries, including banking, insurance, healthcare, and government, We have seen first-hand what happens when agents operate without governance. The risks are not theoretical. They surface in production, often quietly, and compound faster than most organizations expect.
The pattern we see repeatedly is this: an agent is deployed to solve a legitimate business problem. It works. Other teams notice and build similar agents. Within months, the same sensitive data is being accessed by six different agents, each with different permission scopes, different owners, and none of them talking to each other. By the time IT becomes aware, the exposure is already material.
Gartner identifies misinformation, oversharing, and data loss as the primary threat vectors from ungoverned agents. That aligns with what we observe on the ground. But the lived reality in enterprise deployments goes deeper across five dimensions:
1. Agents inherit more access than they need, and nobody notices
In almost every large deployment we have worked on, agents inherit credentials from the users or service accounts that created them. Those credentials are rarely scoped to minimum necessary access. Over time, as the agent connects to more systems through OAuth tokens and API keys, it accumulates a permission footprint far larger than its original design intended. No single person has visibility into the full chain. When that agent is eventually compromised or misconfigured, the blast radius is not one system. It is every system the token chain touches.
2. Compliance frameworks assume humans made the decisions
GDPR, HIPAA, SOX, and most sector-specific regulations were written with human decision-makers in mind. They assume there is a person who accessed the data, a person who made the call, a person who can be held accountable. AI agents break that assumption entirely. In regulated deployments we have audited, agents were processing protected health information, customer financial records, and employee data with no audit trail of what was accessed, no log of what decision was made, and no owner to escalate to when a regulator asked questions. That is not a compliance gap. That is a compliance exposure that can trigger enforcement action.
3. Redundant agents drain budgets in ways that never appear on a single line item
When five teams each build their own version of a document summarization agent, the cost does not show up as one line. It shows up as five separate vendor contracts, five infrastructure footprints, five sets of API tokens consuming quota, and five engineering teams maintaining overlapping codebases. In organizations we have worked with, this kind of redundancy typically accounts for 20 to 35% of total AI tooling spend, none of it visible in a single report.
4. Conflicting outputs erode trust in AI faster than any security incident
One of the most underestimated risks we see is not a breach. It is a contradiction. When two agents trained on slightly different data, connected to slightly different systems, produce different answers to the same business question, the downstream effect is a loss of confidence in AI-generated outputs across the organization. Business users stop trusting the agents. Decisions default back to manual processes. The productivity gain that justified the investment evaporates.
5. Customer-facing agents amplify errors at machine speed
In consumer-facing deployments, a single misconfigured agent does not make one mistake. It makes that mistake at scale, to every user who triggers the same condition, before any human is aware something has gone wrong. We have seen scenarios where an agent with an incorrect policy interpretation provided the same wrong answer to thousands of customers in the time it took the support team to log and escalate the first complaint. The reputational and remediation cost of that kind of incident far exceeds the cost of the governance infrastructure that would have prevented it.
How to manage AI agent sprawl: the Gartner 6-step framework
At the Gartner Digital Workplace Summit in London in April 2026, Max Goss, Sr. Director Analyst at Gartner, presented a six-step framework for CIOs and IT leaders to do exactly that: establish AI agent governance and bring sprawl under control. The steps are intentionally sequential. Each one builds the governance foundation the next step depends on, which means skipping ahead or implementing them in parallel tends to produce gaps that undermine the whole program.
- Establish AI agent governance policies: Define clear rules for when and how agents are built, who can create and share them, and which connectors are permitted. This is the policy layer that every subsequent control enforces.
- Build a centralized AI agent inventor: Use AI TRiSM tools to discover and categorize every agent across all applications, including sanctioned tools and shadow AI solutions. Build adaptive risk-based controls for each agent.
- Define agent identity, permissions, and lifecycle: Assign each agent a unique identity with scoped permissions. Manage access controls rigorously. Establish a formal process to review and retire redundant or dormant agents before they become liabilities.
- Develop AI information governance: Govern what information each agent can access. Manage permissions to prevent oversharing. Archive or restrict data access when data becomes obsolete, out of scope, or no longer required.
- Monitor and remediate AI agent behavior: Establish continuous visibility into agent usage and behavior. Ensure policy compliance, detect anomalous activity, and correct agents that exceed their intended scope or risk tolerance before incidents occur.
- Foster a culture of responsible AI usage: Support employees with training programs and communities of practice. Drive adoption of governance norms until responsible agent deployment becomes the organizational default, not the exception.
Step six is structural, not aspirational. Governance programs that rely entirely on enforcement energy collapse when leadership attention moves to the next priority. Culture change is the mechanism that makes the first five steps self-sustaining.
How Kore.ai helps enterprises control AI agent sprawl
Kore.ai addresses AI agent sprawl through Artemis, its AI-native Agent Platform designed to govern how agents are built, deployed, orchestrated, and operated across the enterprise. Rather than bolting governance after the fact, Artemis embeds it directly into the platform architecture, giving enterprises visibility, control, auditability, and operational consistency as their agent ecosystems scale.
AI agent governance: built in from day one, not bolted on later
Most governance failures happen because agents are deployed first and governed later. Artemis prevents this through Agent Blueprint Language (ABL), a typed, schema-driven language that defines agent behavior, tools, guardrails, permissions, orchestration logic, and handoffs as structured artifacts. Agent definitions are compiler-validated before a single line runs in production. Invalid tool references, broken handoffs, policy conflicts, and orchestration errors are caught early, when they are cheap to fix, not after they have caused an incident.
Runtime policy enforcement for AI agent security and compliance
Relying on an LLM to enforce its own governance is not governance. Artemis separates policy enforcement from model reasoning entirely. Constraints are enforced by the runtime: actions such as block, redact, escalate, or hand off are triggered automatically based on defined policies, not model judgment. Security, compliance, and operational controls hold even when agents are acting autonomously at scale.
AI agent lifecycle management: versioning, auditing, and retiring agents
Ungoverned agents tend to accumulate: deployed, forgotten, and never retired. Artemis treats agents as versioned, governed software assets. Every change moves through controlled testing, staging, and production environments. Rollbacks, approvals, audit trails, and promotion workflows bring software-grade discipline to a space that has historically had none.
Centralized AI agent inventory and control plane for enterprise visibility
Shadow agents and orphaned deployments thrive in the absence of a single source of truth. Kore.ai's Agent Management Platform serves as the centralized control plane for the entire agent ecosystem: unified inventory, ownership tracking, deployment status, lifecycle state, operational health, and governance posture, all visible in one place. When a regulator or auditor asks what agents are running and what they can access, the answer is available immediately.
AI agent observability and audit trails for enterprise compliance
Audit trails that only capture outcomes are not enough for regulated industries. Artemis captures trace events across model calls, tool invocations, routing decisions, handoffs, and policy evaluations, creating a full reasoning path for every decision an agent makes. That level of traceability is what separates a platform built for enterprise compliance from one built for demos.
Multi-agent orchestration governance at enterprise scale
Governing a single agent is manageable. Governing thousands of agents collaborating across workflows is a different problem entirely. Artemis handles it through built-in orchestration primitives: delegation, supervisor patterns, handoffs, escalations, and agent-to-agent collaboration, all with explicitly defined and observable interaction paths. Enterprises maintain control not just over individual agents but over the entire network of agents working together.
By combining Artemis and AMP, Kore.ai gives enterprises the technical governance foundation to scale agentic AI safely and responsibly. The result is not just better oversight of individual agents. It is a governed, observable, and manageable operating model for enterprise-wide AI adoption.
The goal is not fewer agents. The goal is ensuring every agent is visible, governed, accountable, and operating within enterprise-defined controls.
Conclusion
Every enterprise AI transformation eventually arrives at the same inflection point: the moment when the number of agents running across the organization exceeds anyone's ability to account for them. What happens at that moment defines whether agentic AI becomes a strategic asset or an operational liability.
The organizations that reach that inflection point with a centralized registry, governed identities, enforced policies, and a clear lifecycle model will barely notice it. The ones that reach it without those foundations will spend the next several years in remediation mode, explaining to regulators why agents were accessing data nobody authorized, retiring deployments nobody owns, and rebuilding trust in AI outputs that nobody can trace.
The window to get ahead of this is not infinite. The governance decisions made today will determine whether scale becomes a competitive advantage or a compounding risk.
Agent sprawl is the inevitable consequence of successful AI adoption without governance infrastructure. The goal was never fewer agents. The goal was always ensuring that every agent, at any scale, remains visible, accountable, and under control.
FAQs
1. What is AI agent sprawl, and why is it a problem for enterprises?
AI agent sprawl is the uncontrolled proliferation of AI agents across an organization without centralized visibility, governance, or ownership. It becomes a problem because ungoverned agents can access sensitive data without audit trails, create compliance violations under GDPR, HIPAA, or SOX, accumulate excessive permissions through OAuth tokens, and produce conflicting outputs across fragmented workflows. As deployment barriers drop to near zero, the gap between how fast agents are built and how well they are governed is the single biggest risk multiplier in enterprise AI today.
2. How do you detect and stop AI agent sprawl in your organization?
Detection starts with a comprehensive agent census: automated scanning of cloud environments, SaaS integrations, OAuth connections, and API activity to surface every active agent, including those deployed outside official channels. Key signals that sprawl is underway include the inability to count active agents with confidence, agents discovered only during audits or incidents, and multiple teams building overlapping solutions. Stopping it requires a centralized agent registry, tiered approval workflows, identity-based access controls with least-privilege permissions, and lifecycle management that automatically surfaces dormant agents for retirement.
3. What is the difference between AI agent sprawl and shadow AI?
Agent sprawl is the operational inventory problem: agents deployed without coordination, documentation, or lifecycle management, even if individually sanctioned by IT. Shadow AI is the security consequence of that gap: tools and agents operating entirely outside oversight because employees route around controls that feel too slow or restrictive. The relationship is directional. Unchecked agent sprawl produces shadow AI. This is why blocking agent use without providing a governed alternative backfires: it drives deployment underground, creating far greater security and compliance risks than the sprawl it was meant to prevent.
4. How does Kore.ai help enterprises manage AI agent sprawl?
Kore.ai is named by Gartner as a sample vendor in the Agent Management Platform category and addresses sprawl through two integrated capabilities: Artemis, its AI-native Agent Platform, and its Agent Management Platform (AMP). Artemis embeds governance directly into how agents are defined using Agent Blueprint Language (ABL), validating behavior, permissions, and orchestration logic before anything reaches production, while enforcing security and compliance policies at runtime through the platform rather than the model. AMP provides the centralized control plane: a unified inventory tracking every agent's ownership, permissions, lifecycle state, and operational health across the enterprise, with reasoning-level observability that produces audit-ready evidence for GDPR, HIPAA, SOX, and the EU AI Act. Together, they give enterprises a governed, observable, and scalable operating model for agentic AI, one where every agent is visible, accountable, and operating within enterprise-defined controls.
