Don't Let Each Department Have Its Own AI! What Businesses Really Need is a Shared AI Infrastructure

Don’t Let Every Department Build AI in Isolation

Artificial Intelligence and Machine Learning | Appar Technologies Co., Ltd. | 2026/08/20

The most common problem companies face when implementing AI Agents might not be employees' reluctance to use them, but rather everyone starting to use them too quickly. When customer service, finance, engineering, sales, and regional branches each build their own Agent, companies quickly face a new form of information silos: each Agent stores credentials, connects systems, sets permissions, and redundantly develops the same capabilities. The latest practices from AGCO, Microsoft, and Amazon Web Services show that if companies want to expand Agents from sporadic experiments to true operational capabilities, they must establish a shared AI infrastructure.

In 2026, American agricultural machinery manufacturer AGCO encountered a problem many companies would envy when promoting corporate AI: employees' interest in AI Agents far exceeded expectations. In a meeting with about 2,200 managers and employees, the company asked who would be willing to start learning to build Agents, and about 900 people raised their hands. This internal builder community later expanded to about 2,000 people, resulting in hundreds of Agents built by employees or teams.

AGCO did not treat this enthusiasm as a simple tool procurement plan. The management team realized early on that if each employee sought tools independently and connected company data on their own, it might just replace past Shadow IT with new Shadow AI. Therefore, AGCO allowed employees to propose work friction points, which the AI team and experts then translated into suitable Agents, processes, or automation; the company deliberately built a framework that allows multiple Agents to continuously join, rather than a series of unrelated small projects.

This approach has already begun to affect AGCO's manufacturing and quality management processes. Previously, some quality and warranty cases might take weeks or even months, with a few experts organizing information, verifying data, and coordinating next steps across multiple teams; after introducing Agents, the company hopes AI will bring information from different systems into a single process, allowing issues to be read, verified, and advanced to the next stage more quickly. The real value is not how fast a chatbot can respond, but that the entire organization starts building reusable AI capabilities.

The same issue is emerging in other large enterprises. Microsoft describes the rapidly increasing Agents as a new corporate workload, while Amazon Web Services directly points out that when companies start having hundreds or even thousands of Agents, three core problems arise: not knowing which Agents currently exist, inability to consistently manage which capabilities can be offered externally, and different teams repeatedly building already existing functions. Therefore, AWS launched the Agent Registry in 2026, attempting to enable companies to centrally discover, share, and manage Agents, tools, and capabilities.

These cases reveal an important turning point. In the first phase of corporate AI, the competition was about "who can create an Agent faster"; in the next phase, the real question becomes "how can the company still quickly and safely build the hundredth Agent." As the number of Agents begins to increase, the quality of a single product is no longer the only issue, and the architecture itself will start to determine the expansion speed.

From Employee-Driven Innovation to Agent Sprawl

Corporate AI adoption usually doesn't spiral out of control at the start. The first customer service Agent might only need to connect to the customer relationship management system; the second financial Agent connects to the enterprise resource planning system; the engineering department builds a programming development Agent; the marketing department procures a set of tools on its own. Each project seems reasonable on its own and can often prove its value in a short time.

The problem is that as these Agents gradually increase, each team starts to repeatedly handle the exact same things. Everyone needs to figure out how to log into the company system, how to store credentials, how to describe APIs, how to leave operation records, and how to control permissions. Companies that originally hoped AI would eliminate repetitive work end up creating a lot of repetitive work in "building AI."

Amazon Web Services calls this situation Agent Sprawl, where the number of Agents rapidly increases but lacks a common inventory, governance, and reuse mechanism. AWS pointed out when introducing the Agent Registry that without a central directory, teams can easily rebuild another set of Agents or tools because they are unaware of similar capabilities already developed by other departments, ultimately increasing maintenance costs and technical debt.

AGCO's experience shows another path. The company did not stop employees from building Agents but established a governance path that gradually transitions from personal experiments to formal corporate environments. Employees can still experiment quickly, but larger Agents that affect corporate processes are collaboratively developed by central teams and experts. This model keeps innovation decentralized while gradually centralizing infrastructure and governance.

Redefining Corporate AI Infrastructure

Solving Agent Sprawl does not mean all Agents must be developed by a central AI department. This approach could revert to the bottleneck of traditional IT projects: every department queues up for any needs, and the central team never finishes. What truly needs centralized management is not "who can build Agents," but the underlying capabilities everyone repeatedly needs.

For example, identity authentication, permission management, credential storage, enterprise tool directories, connection methods, traffic control, audit records, and monitoring do not need to be redeveloped by every Agent team. The more standardized these capabilities are, the more business units can focus their time on truly differentiating areas, such as business processes, prompts, data quality, human-machine collaboration, and user experience.

Microsoft has also begun to adopt a similar direction in its Agent management practices. Microsoft Digital uses a central control layer to understand Agents built on different platforms, who built them, who can use them, and what data they can access; managers do not have to search for Agent status across multiple systems. This centrally observable, decentralized innovation model is likely to become an important feature of corporate Agent architecture.

Defining the Common Control Layer

From the current architecture of large enterprises and cloud platforms, shared AI infrastructure is typically not a single product but a set of common services located between the Agent and enterprise systems. Different enterprises can place it in various positions based on their existing architecture, such as:

• Enterprise AI platform, maintained by central IT or AI teams
• API Gateway or MCP Gateway type shared connection layer
• Identity and access management platform
• Agent Registry or enterprise tool directory
• Cross-departmental AI governance and observability platform

No matter which product or organizational method is adopted, their purpose is similar: to extract problems that each Agent needs to solve repeatedly and turn them into capabilities that the company can reuse. Amazon Bedrock AgentCore Gateway is a concrete example, allowing Agents to connect tools, other Agents, and models through a single secure entry point, and centrally handle inbound and outbound authentication, OAuth, credential storage, tool integration, and auditing capabilities.

A mature enterprise AI shared layer typically needs to handle the following tasks:

• Create a common directory of enterprise-available Agents, MCP Servers, and tools
• Unified handling of Agent and user identities
• Manage API Keys, OAuth Tokens, and other credentials required by enterprise systems
• Control which Agents can call which tools
• Keep audit records of every tool usage and system operation
• Provide monitoring of traffic, cost, errors, performance, and abnormal behavior
• Establish mechanisms for formal launch, version updates, deactivation, and lifecycle management

With this layer, the way Agents are developed will change significantly. Developers no longer need to ask "How do I re-integrate the enterprise resource planning system," but rather "What capabilities does the company already provide that I can use." This difference may seem like just writing fewer integration programs, but it actually determines whether a company can expand from ten Agents to a hundred Agents.

Characteristics of a Good AI Infrastructure

After observing the development of Microsoft, Amazon Web Services, Google, and the MCP ecosystem, we can see that enterprise shared infrastructure is gradually forming several common characteristics. These capabilities are different from the reasoning ability of a single model and do not lose value just because a different large language model is used six months later:

1. Model Neutrality: Enterprise underlying capabilities should not be tied to a single model. Today, OpenAI can be used, tomorrow Claude, Gemini, or self-built models can be added, and enterprise systems do not need to be completely redone.
2. Tool Reusability: The same "query customer," "create work order," "get inventory" capabilities should be usable by different Agents, rather than each Agent re-integrating individually.
3. Identity Consistency: Agents, regardless of which platform they are built on, should be identifiable, authorized, and trackable by the enterprise. Microsoft Entra Agent ID is already developing in this direction and supports Agents built on non-Microsoft platforms.
4. Centralized Governance, Decentralized Innovation: Business units can still create Agents that meet their needs, but security, permissions, data, and audit policies should be provided by a common platform.
5. Complete Observability: Enterprises need to know not only how many tokens a model used but also which Agent used which tool, the success rate, what errors occurred, and the actual business value generated.
6. Replaceability and Extensibility: Adding new Agent platforms, new systems, or new tools should not require rewriting the entire architecture.

This design is particularly important because the speed of change in AI models is much higher than that of enterprise core systems. Customer relationship management systems, enterprise resource planning systems, and internal databases may be used for more than ten years, but the model suppliers used by enterprises may change several times within a year. If enterprises hard-code all integrations into an Agent of a specific model supplier, every platform strategy change will result in redevelopment costs.

Therefore, what truly holds long-term value is not "which model the company used today," but the tools, permissions, data governance, and process capabilities that the enterprise gradually accumulates. Models can be changed, Agents can be changed, and even Agent platforms can be changed; what the enterprise truly does not want to keep redoing is that entire set of company-specific capabilities.

How to Build an Enterprise Shared Agent Platform

When building this architecture, enterprises do not need to create a massive "AI operating system" on the first day. A more reasonable approach is to start with the most frequently recurring integration problems. For example, when three different teams all need to query customer data, it should be considered whether "query customer" can be turned into a common capability, rather than letting a fourth team re-integrate the customer relationship management system.

The second step is to establish a registry of Agents and tools. Enterprises need to know which Agents currently exist, what tools are available, who is responsible, and which are still operating in the formal environment. AWS Agent Registry is a central directory developed for this issue: both users and Agents can search for existing Agents, MCP Servers, tools, and capabilities to avoid unnecessary duplicate development.

The third step is to separate credentials and permissions from the Agent itself. If each Agent keeps its own enterprise system passwords, API Keys, or Tokens, it becomes difficult for the company to manage revocation, updates, and audits. Gateway-type architectures allow Agents to only know "I need to call a certain tool," while the enterprise credentials needed to connect to the backend system are securely managed by the shared layer. AWS AgentCore Gateway has already made Secure Credential Exchange and OAuth management core capabilities.

The fourth step is to establish a fast track for low-risk Agents. As long as the tools, data, and identity mechanisms approved by the company are used, and there are no high-risk actions, the team can quickly move the Agent into the production environment. When there is a need to access new sensitive data, make payments, delete, or perform irreversible actions, a more stringent review process is required. This risk grading approach is more efficient than sending all Agents for IT department review.

Finally, companies must establish shared metrics. The amount of time an Agent saves employees is just one indicator. More importantly, companies should focus on reducing redundant integrations, shortening the time for new Agents to go live, increasing the reuse rate of existing tools, and quickly tracking incidents. When the platform makes it easier to build the next Agent compared to the previous one, the company truly accumulates organizational capabilities in artificial intelligence.

The AI Infrastructure Race is Heating Up

This field is worth the attention of companies now because large tech companies have started to extend from "providing models" to "providing Agent infrastructure." Microsoft has established Agent 365 and Entra Agent ID; Amazon Web Services has developed AgentCore Gateway and Agent Registry; Google Cloud has begun offering enterprise-level MCP Server and related governance capabilities. This indicates that the market focus has shifted from a single model competition to who can manage a large number of Agents, tools, and enterprise systems.

For companies, the real investment should not be in a specific Agent project itself, but in a framework that can continuously absorb new models, tools, and workflows. A customer service Agent established today might be replaced by another product in two years, but the customer data, permission rules, customer service policies, and enterprise tools it uses remain long-term assets of the company.

The maturity of a company's artificial intelligence may ultimately not be determined by "how many Agents" it has, but by "how easy it is to add a new Agent." When each department nurtures its own AI, the company ends up with many AI projects. When data, tools, permissions, and governance can be repeatedly reused, the company truly begins to build its own AI infrastructure.