Enabling Agentic Fabric for Enterprise AI: Expanding intelligence beyond the platform boundary

[Anthony JC]

As organizations shift toward agentic AI, the limiting factor is no longer model performance. The challenge is enabling intelligent agents to access enterprise context without breaking architectural integrity. In large environments data/ services may live outside ServiceNow: regulatory data, financial engines, policy systems, specialized AI services, and even other autonomous agents.

Pulling everything into the platform is not sustainable. It increases data gravity, governance overhead, ownership ambiguity, and long-term maintenance burden. What ServiceNow needs is not more ingestion, but a mechanism for precision context access at runtime.

This is where the platform’s context access layer, often referred to as the Model Context Protocol, plays a critical role. Instead of transferring or duplicating data, it provides controlled access to capabilities, intent, and context that support intelligent decision making without platform bloat.

At a platform architecture level, this capability delivers two core advantages:

• Virtual access to external context without replicating, transferring, or assuming ownership of data.
• A unified invocation pattern that can support system-to-agent and agent-to-agent interactions, contributing to a scalable agentic fabric.

This positions ServiceNow to operate as a control point for agentic AI workloads.

The architectural shift introduced by Agentic AI

Traditional ServiceNow implementations follow a familiar pattern. Data is, integrated, normalized, stored in tables, and then acted upon through workflows. Even when integrations exist, the assumption is that relevant data eventually lives inside the platform. But that is not the case when it comes to agentic AI.

Agents do not simply execute predefined steps. They evaluate context, infer intent, and decide which actions to take. To do this effectively, agents require access to information that often exists outside ServiceNow, such as external systems of record, policy repositories, regulatory sources, financial engines, and specialized AI services.

Attempting to ingest all this information into ServiceNow is neither practical nor necessary. It increases data gravity, introduces latency, duplicates ownership, and complicates governance. What agentic systems require instead is a way to access context at runtime without owning it. This is the architectural gap MCP fills.

Reframing MCP as context access rather than data movement

This capability is not about moving data from one system to another. It is about making context accessible to AI agents in a controlled and standardized way.

MCP defines how an AI runtime discovers and invokes external context providers. These providers are intended to expose capabilities aligned to a specific intent, supporting bounded operations rather than exposing raw tables or schemas.

This distinction matters in ServiceNow. The platform already offers robust integration options through APIs and integration tooling. What agentic AI needs is a way to retrieve context on demand without permanently storing or synchronizing that data. MCP provides that capability.

Context access compared to Data Fabric and Knowledge Graph

There is a tendency to compare the context access layer (commonly known as Model Context Protocol) with ServiceNow Data Fabric and Knowledge Graph. These capabilities serve distinct roles.

Data Fabric and Knowledge Graph support internal unification. They align and relate data that already exists within ServiceNow, so AI, workflows, and human users operate from a consistent internal semantic model. Their purpose is to interpret what the enterprise already owns: services, configuration items, dependencies, knowledge, and people context.

The context access layer operates outside that boundary. Instead of ingesting or linking external information into the platform, it retrieves only the context required at runtime directly from the source system. No replication, no synchronization, and no change in ownership. The agent receives only the context required to reason and act, and the original system remains authoritative.

MCP within the ServiceNow agentic architecture

From a ServiceNow architectural standpoint, MCP functions as a context ingress layer for AI agents.

ServiceNow provides the agent runtime, workflow orchestration, case and record management, and governance boundaries. MCP provides a standardized way to access external context and capabilities without embedding that complexity inside agents or workflows.

This separation allows ServiceNow agents to remain focused on reasoning and decision making, while MCP providers manage access to external systems. This positions ServiceNow to operate as a control point for agentic AI workloads, where reasoning and governance remain centralized while context stays with the source.

Connect agents across platforms

In this architecture, an MCP server exposes a single provider endpoint that presents one or more capabilities for agents to invoke. Each capability represents a function the provider can perform, such as policy evaluation, risk scoring, or domain specific reasoning. The endpoint is simply the access point; the capabilities behind it define what the provider can deliver.

A responding agent can operate as an MCP provider by registering its capabilities behind that endpoint. A calling agent then discovers the available capabilities through the provider definition and invokes the one it needs, using the same endpoint while specifying the required capability in the request.

When the calling agent invokes a capability on the provider, it receives a structured response. It does not require visibility to the provider’s internal reasoning or how the outcome is produced. From the caller’s perspective, it behaves like a service invocation: request the capability, receive the result, and continue the current flow of reasoning or execution.

As multiple capabilities can be published behind a single MCP provider endpoint, capability expansion can scale in a controlled manner. New capabilities can be added without changing the endpoint itself, preserving a stable access interface. Agents can reuse existing providers, expand capability sets over time.

In ServiceNow, this pattern supports the formation of the agentic fabric: a connected network of calling agents and responding agents, linked through MCP providers and capabilities, where expertise can access as a service.

Why this matters for enterprises

The combination of the context access layer and ServiceNow’s agentic architecture expands what AI can realistically accomplish in the enterprise. Instead of limiting an agent to information stored inside ServiceNow, the agentic fabric allows it to reach external context, request specialized capabilities, and apply reasoning across systems when needed. This increases the functional reach of AI without forcing data into the platform or increasing the data footprint that must be governed, secured, and maintained.

Benefits for enterprise

• Expands AI capability by allowing agents to use external context and expertise without waiting for data to be migrated or remodeled in ServiceNow.
• Protects data ownership and compliance rules because information is accessed at runtime while security, governance, and jurisdiction remain with the source system.
• Reduces platform overhead and complexity by avoiding continuous ingestion, synchronization cycles, or redundant storage.
• And Improves delivery speed and time to value.

This architecture gives the enterprise a practical path forward: scale AI capability now, keep data where it belongs, and let ServiceNow become the place where intelligent decisions are executed with velocity and control.