AI agent sprawl: What it is and how to control it

Sprawl occurs when AI agents—autonomous systems that make decisions and take actions with minimal human input—are deployed without a unified strategy or strong governance practices. AI agent sprawl creates an ecosystem of redundant and fragmented agents across teams and functions. Much like AI sprawl or app sprawl before it, AI agent sprawl occurs when speed eclipses visibility: Individual teams deploy agents to automate tasks or manage workflows without a cohesive organization-wide program.

According to Gartner, by 2028 the average Fortune 500 enterprise will use over 150,000 AI agents. But, according to the firm, only 13% of organizations believe that they have the right AI agent governance in place. This data points to a simple yet pressing reality. Agents, particularly in low-code or no-code environments, are easy to build, but they are much harder to deploy, operate and monitor responsibly.

Sprawling, unmanageable ecosystems of AI tools can also significantly increase costs. According to the US Chamber of Commerce’s research, 58% of small businesses have adopted generative AI and some consultants report small firms with tight budgets spending thousands of dollars a month on a handful of redundant AI writing tools. Which isn’t to mention the hidden cost of employee who, rather than seeing productivity increase from their use of AI, realize the opposite: Toggling between applications and platforms throughout the day significantly slows down work.

When individual teams such as marketing, finance or customer support build agents, those agents might not be visible to other parts of an organization. This creates a smattering of agents with disconnected capabilities rather than a coherent system. According to research from the IBM Institute for Business Value, only 18% of organizations maintain a current and complete inventory of their AI agents, stymying integration efforts. Similar problems might be solved redundantly, with minimal knowledge transfer between systems.

Agent sprawl creates systems that operate independently, with no shared context or mechanism for resolving issues when outputs overlap. Without centralized orchestration, agents might duplicate work or create unintended feedback loops. These duplications and loops rapidly compound errors across interconnected systems. 

Given the rate of agentic AI adoption, many organizations lack holistic policies and processes to manage large agent networks responsibly. This means that there might be:

• No standard approval workflow before agent deployment

• No defined ownership for if and when errors occur

• A lack of lifecycle processes for retiring agents when they’re no longer necessary.

Without organization-wide governance practices, risks increase exponentially. 

AI agents without strong security and compliance controls might access sensitive data without proper authorization or bypass audit trails. When agents act autonomously at scale, a single misconfiguration might become an organization-wide liability.

Risks like these are particularly pressing in areas like finance or healthcare, where they could accidentally expose protected information. Regulatory frameworks such as HIPAA or GDPR can become impossible to satisfy when no single source of truth exists addressing what agents access or how they make their decisions. 

When different teams build similar agents—or even identical agents—each team incurs its own infrastructure cost. Over time, the price of compute and API tokens, as well as third-party licensing costs, compounds. And AI systems that aren’t properly sunset can continue to use resources after they’re retired, draining budgets and eating up valuable resources. 

When agent networks are fragmented and siloed, they’re more difficult to scale effectively. Sprawling agent ecosystems can be hard to maintain and improve. Agents built in isolation might replicate effort, but more importantly, they lack shared tools. This shared tooling has the potential to transform practices across departments and allow agents to be more easily monitored and logged.

Agent sprawl also results in dramatically slower incident responses if an agent makes a mistake and a responsible team isn’t quickly identified. And responding to incidents stemming from compromised agents or unauthorized data use can create serious risks when complex, interdependent and unexplainable agents are involved.

According to Salesforce’s 2026 Connectivity Benchmark report, the average organization uses 12 or more AI agents, but 50% of those agents operate in siloes rather than as part of a coordinated system. Without intentional coordination, the same data pipelines might be built multiple times, and each system of agents maintains its own version of the truth. This can create conflicting outputs.

Data fragmentation complicates lineage tracking, making it difficult to audit which agent made a particular decision. It also prevents organizations from realizing the true value of autonomous cross-departmental ecosystems sharing appropriate data seamlessly. 

Agent sprawl and shadow AI are related, but distinct terms. Shadow AI describes the use of unauthorized tools by employees. For example, a marketing lead using a personal LLM account such as ChatGPT to process work documents. AI agent sprawl, in contrast, describes a structural phenomenon. Even sanctioned, IT-approved agents can contribute to sprawl if they’re deployed without coordination.

But AI agent sprawl can sometimes inadvertently lead to an increased change of shadow AI, as Gartner found. “As CIOs and IT leaders see an explosion of AI agents across their organization,” wrote Max Goss, senior director analyst at Gartner, “many are contending with an ungoverned sprawl of agents that expose their organization to a range of risks, including oversharing and data loss.” Many enterprises, he added, tend to resort to blocking or restricting agent use. Unfortunately, this tactic tempts employees to use shadow AI instead, creating far more serious security and compliance risks.

AI agent sprawl reflects the latest chapter in enterprise technology outpacing the organizational capacity to govern it. It’s a pattern that has repeated with increasing speed. For instance, SaaS sprawl and shadow IT resulted from cloud technology making new software far easier to adopt, often without the knowledge of centralized IT departments.

Agentic AI’s potential to transform workflows and create powerful human-AI partnerships has resulted in wide-scale adoption. According to internal research from IBM, the majority of enterprises are already using AI agents in some capacity.

But given the proliferation of AI tools capable of creating agents quickly, building agentic tools no longer necessarily requires a software engineer or a lengthy fine-tuning process. Tools such as Microsoft’s Copilot Studio and Salesforce’s AgentForce support low- and no-code agent development options—powerful solutions that nonetheless encourage rapid deployment across departments. To wit, the same internal IBM research found a large number of enterprises report AI sprawl is already raising security risks and resulting in unnecessary complexity.

The implications are significant: Nearly every department in a large enterprise has the capacity to deploy autonomous AI agents, but the mechanisms to control and govern these sweeping networks are lacking. Still, the democratization of AI and agentic platform development, along with the real business gains promised by such technologies, make them difficult to abandon. Governing AI agents responsibly requires a centralized and intentional approach that monitors and optimizes agent behavior at scale.

The lack of a scalable model for controlling AI use across an organization also prevents enterprise-wide coordination. “There’s not a client that doesn’t have at least 60 random acts of AI happening throughout the organization with shadow IT, shadow AI,” Matt Kosinski said on a recent episode of IBM’s Mixture of Experts podcast. “And every department and executive [is] going, I’m actually the one that’s leading this from procurement, or from HR or from this business unit.” 

It’s difficult to govern what’s impossible to see. Successful attempts to reign in agent sprawl begin with a comprehensive inventory: This might include an automated scan of cloud environments and APIs to surface all active agents. Mechanisms for discovering informal autonomous agents operating outside of the official channel should also be included. It can be useful to establish a continuous discovery process instead of a one-time audit, as new agents will be continuously entering the ecosystem. During this time, a successful sprawl control program will include the establishment of an enterprise-wide centralized inventory to track agents’ owners, purpose and data access permissions. 

Once agents are a known entity across an organization, organizations should define clear rules around who can create, deploy and share agents—as well as establish compliance rules for AI tools. Data use, rate limits and connecting tools should be carefully monitored, and agent inventories can be used to build adaptive controls that enforce the right policies based on the level of risk an agent carries. 

Standardization helps reduce future sprawl. Limit how many platforms are used for creating agents to reduce architectural complexity, and establish ongoing visibility into agent usage to help ensure standardized policy compliance. Real-time dashboards can help detect anomalous behavior and correct agents that misbehave or exceed their intended scope. When building a new agent is easier through an enterprise’s standard path than outside of it, governance can become self-reinforcing. 

An effective response to agent sprawl combines specific toolkits with an intentional organizational process. Increasingly, the most powerful solutions integrate seamlessly: For instance, a coding tool that works in tandem with an orchestration layer and enterprise control panel to continuously build, monitor and optimize agentic AI across the entire development lifecycle.

Enterprise control planes are centralized management layers that give organizations visibility and control over autonomous systems. These layers sit on top of AI agents, LLMs and other AI tools, acting as a kind of mission control. Control planes typically allow enterprises to observe, configure and govern autonomous systems from a single source. 

Combatting shadow AI and agent sprawl requires powerful, standardized and secure coding partners. Today’s enterprise AI development partners, such as IBM Bob, are built on structured frameworks and embed into every step of the software development lifecycle—from planning through testing and operations. By providing standardized transparency and security controls from day one, these tools allow organizations to scale quickly and stay in control while significantly reducing sprawl.

In Bob’s case, the developer tools integrate seamlessly with watsonx Orchestrate, a centralized control plane. Working in tandem, these systems can address real-time problems, suggest fixes and build new agents addressing them. They also create self-documenting agentic workflows, ensuring each action is auditable and traceable.

AI TRiSM tools—a category formalized by Gartner—provide continuous monitoring of AI behavior. Typically, they detect anomalies, enforce guardrails and surface policy violations. These tools treat AI agents as observable and auditable entities, applying runtime controls that don’t require agents to be entirely rebuilt. 

Governance frameworks establish an organization’s rules for who can deploy an agent and what approval framework they should follow. They might also address which data sources are permissible to use and what performance standards should be met before an agent is released. The most effective governance frameworks establish clear ownership and permissions, define escalation paths and seamlessly integrate with existing enterprise risk processes instead of operating as a parallel process. 

An agent registry acts as a single source of truth by cataloging every AI agent deployed across an organization. Registries document agents’ purpose, owner, data access, model version and operational status, among other variables.

These databases transform an AI ecosystem into a known and manageable inventory. Modern inventories are often dynamic—agents are registered on deployment and update their status automatically—rather than relying on manual documentation that is more error-prone. 

Often, sprawl can be a decommissioning failure: Agents are deployed and never retired. Lifecycle management tools enforce a defined system from the moment development begins, triggering reviews at regular intervals and automatically surfacing agents that have gone dormant. 

Rather than letting agents operate in isolation, orchestration platforms coordinate multi-agent workflows, defining how agents communicate, share context, hand off tasks and escalate to humans. By making inter-agent relationships explicit and observable, orchestration reduces redundancy and provides a natural integration point for monitoring, optimization, logging and access control.