A marketing team asks the AI assistant inside its content platform to build the campaign page for the spring product line. The assistant writes the copy in seconds; however, it cannot pull the product details, as those live in the commerce system. 

The assistant's reach ends at the platform edge. 

Most organizations know this wall boundary, because the agents they’ve invested in arrived one per product. Each performs well inside its own walls, which helps, but only marginally, since the work marketing needs done spans all go-to-market platforms.

Gartner surveyed 413 marketing technology leaders between June and August 2025 and found that most were either piloting AI agents or already running them in production. Those same leaders report that the agents underdeliver: 45% say the vendor-offered AI agent capabilities they are running do not meet company expectations of promised business performance. The budget was committed before the capability was proven.

A major factor in the underperformance and, thus, disappointment of most AI add-ons is what the agent is able to reach.

Frans Riemersma of MartechTribe describes AI arriving inside business software in three distinct degrees.

At the first degree, AI improves a single module. It summarizes analytics, drafts copy, or suggests tags. The AI cannot leave its module. 

At the second degree, a vendor supplies an agent that moves across several modules of its own suite, coordinating work that used to require clicking between screens. This agent stays within the vendor ecosystem.

Both degrees deliver real value. Both agents stop at the same boundary: the product itself, drawn by the vendor who sold it. 

An organization running a heterogeneous tech stack, with a content platform, commerce engine, and a customer data platform from a diversity of vendors, has effectively purchased three AI agents only able to see a third of the full picture. No amount of prompting will widen their view because the limit lives in the architecture.

Half of the leaders in Gartner's survey say their organizations lack the technical and data readiness required for agent deployment. Therefore, while reach explains part of the disappointment, another element is what the agent has to work with. 

For example, an agent asked to build a localized product page needs the product record, approved images, regional pricing, and brand rules. Organizations traditionally store this knowledge across multiple systems, which makes sense in structures built for humans clicking on screens. An AI agent is a different kind of user; it needs content and data in a form it can retrieve on its own. 

When an agent is handed governed data with no shared meaning behind it, the output might look precise and pass at first glance, but it creates a risk no company should bet its reputation on.

For most of the past decade, connecting one system to another meant commissioning a custom integration, which is why marketing requests that crossed system boundaries turned into engineering projects with their own timelines.

This paradigm has shifted as software has converged on a shared way of exposing its capabilities to an AI assistant, called the Model Context Protocol, or MCP. Capabilities that once required opening a specialist platform can increasingly be reached from a general AI environment, while the platform itself remains the system of record.

As a result, connecting the system to an AI agent is no longer a bespoke engineering project. The interface still supplies the navigation and functionality its human users need to build in clicks and keystrokes; alternatively, the agent navigates through the back door to build from the operator’s intended outcome.

When it comes to housing the MCP connection, the choice is between rebuilding it inside every product that wants to reach the others, or building it once in a shared place.

Uniform is that shared place, connecting external systems through a single, unified, codeless layer, with maintained connectors for both headless and monolithic content management systems, digital asset managers, commerce and product information systems, customer data platforms, and analytics tools. 

The agent working in this layer can see the catalog, assets, and customer data at the same time, so the campaign page that used to require a development request is assembled in one place by the person who wants it. 

The outcome for an engineering team is that each system connection is configured once and reused across projects, eliminating the queue of one-off connector requests that AI adoption otherwise generates. 

The systems continue doing what they were purchased to do, and marketers finally perform at top speed through a boundless AI agent that keeps its promise to marketing technology leaders who invest in it.