Fortune 500 companies are expected to operate more than 150,000 agents by 2028, according to an analysis from Gartner.
If achieved, it would represent a 10,000-fold increase compared to 2025, when enterprises averaged just 15 agents. Indeed, this shift toward agentic systems is the most significant architectural evolution of our lifetime.
With adoption accelerating so rapidly, governance is rising to the top of the AI agenda. Businesses of all sizes are seeking safeguards and systems of control to ensure they can trust the agents they work alongside, yet current methods often fail.
The data speaks for itself: although almost every global enterprise is using AI agents, only 12% have introduced a centralized governance approach. For most, governance remains piecemeal, exacerbating the chaos it is intended to control.
This disconnect raises a critical question: as AI scales rapidly, how can organizations maintain governance to manage it responsibly? Nearly every leadership team is facing the same problem: they want to harness AI’s full potential but also maintain control.
AI enthusiasm leads to AI sprawl
While most businesses recognize the value of agentic AI, and are experimenting and deploying various agents across their organisation, the risk of AI sprawl is high. The growing mix of customer-built and pre-built agents creates disparate agentic tools with no centralized strategy.
As enthusiastic employees develop their own agents – an approach which is increasingly common as the technology becomes more accessible – AI usage becomes siloed and inconsistent across the business.
This type of AI sprawl drives diverging standards and duplicated efforts, with research showing 94% of businesses are experiencing increased complexity, technical debt and security risk.
The autonomous nature of agentic AI, where systems interact with data and other agents in real time, means a lack of governance in the early stages can see AI sprawl quickly spiral out of control.
Fragmented governance is ineffective
Forward thinking businesses are investing time and effort into new compliance processes and building better safeguards to ensure AI outputs can be trusted. One such method is the use of human-in-the-loop models for key decisions, an approach currently used by 52% of businesses.
However, as AI scales further and more rapidly, new challenges will emerge. There is a risk that human-in-the-loop methods lead to unintended inconsistency, where employees spanning different teams, regions or departments introduce uncoordinated rules for access, security and usage.
Instead of enforcing control, this has the opposite effect. Businesses are then left grappling with governance systems which reinforce the fragmentation they were trying to overcome.
Centralized rules, centralized standards, centralized knowledge
The only effective way to maintain control of scaling AI is to build governance into the AI itself. Governance must be designed as part of the foundations of an agentic system; it cannot be added as an afterthought.
To ensure meaningful control, enterprises require centralized visibility into which agents are running, how they are connected and where their dependencies lie. This represents a major opportunity, given that only 12% of businesses had introduced a centralized governance approach as of January 2026.
Historically, manual governance fails because enterprises cannot keep pace with the speed and scale at which AI agents interact. One of the core governance challenges is coordinating agents so they do not duplicate work or conflict with one another.
This requires orchestrating agents through a central neutral system layer that connects them to a real-time understanding of enterprise architecture and its operational parameters, ensuring outputs remain secure, compliant and coherent.
By ensuring there is a centralized governance layer built into the enterprise, all agents can follow the same rules, comply with the same standards and draw from shared knowledge. The governance system can then evolve and grow alongside the business itself.
Governance doesn’t need to be slow to be successful
The modern enterprise is already shaped by legacy systems, fragmented architectures and technical complexity, but it is not inevitable for AI to follow the same path.
The explosion of agentic tools offers enormous potential for business productivity, even if it does not automatically create coherent, high-quality architecture. The biggest challenge organizations face right now isn’t the capability of the models themselves – it’s connecting AI to where real work actually happens inside the enterprise.
It is now up to each organisation to pick and choose the agents best suited to their organisation and prioritize their integration in a unified way. This means moving away from old habits – often where governance is added incrementally every time a new agent or application is introduced – to remove the risk of fragmentation and AI sprawl.
The optimal approach is one which works with the unified and agnostic governance layer built into the foundations of the AI system to combat technical debt at scale. Only then will enterprises be able to harness AI’s full long-term potential in a sustainable manner.
Governance should not restrict the speed of AI adoption; it can scale alongside it. The successful enterprise of the future depends on its ability to approach AI with speed and efficiency without compromising trust.
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