Remote MCP is the key to secure and scalable AI for the enterprise
A closer look at organizations need to take agentic AI from an intriguing experiment to an enterprise-grade reality
A guest post by Mohith Shrivastava of Salesforce.
Agentic AI has proven its value for rapid proof-of-concept work and zero-to-one ideation. However, taking these powerful workflows from an isolated workstation to a live production environment has been fraught with challenges. The initial, local-first implementations of protocols like the Model Context Protocol (MCP) left technical leaders wary, and for good reason. How can you securely manage AI agents that have direct, often unaudited access to production systems? How do you scale this capability across a team consistently?
The answer lies in an architectural shift: moving from local MCP instances to remote, platform-hosted MCP servers. This evolution provides the security, governance, and infrastructure necessary to take agentic AI from an intriguing experiment to an enterprise-grade reality. This is the sophistication that agentic systems have lacked, and it's causing leaders who once shied away to take a serious second look.
From local playgrounds to enterprise production
A local MCP server, running on the same machine as the AI client, certainly has its place. For a user testing a new integration or an analyst working with highly sensitive files that must never leave their machine, a local server offers undeniable benefits. It delivers lightning-fast speed, gives the user direct control over processes and credentials, and can even function offline for local tasks.
However, when scaling from an individual's workstation to an enterprise-wide solution, this local-first model presents significant production hurdles:
Security risks: A local MCP server places sensitive credentials and direct API access on a user's machine, creating a broad and difficult-to-monitor attack surface. An agent with overly permissive access could inadvertently expose data or execute harmful commands.
Governance and maintenance gaps: Without a central point of control, enforcing business rules or auditing agent actions is nearly impossible. Furthermore, the maintenance burden—including complex setup, updates, and resource management—falls entirely on the individual user.
Scalability and accessibility issues: A local server is a bottleneck. It is "stuck on one machine," making its tools inaccessible to web-based AI agents or other team members, and creating inconsistent environments across an organization.
Remote MCP servers fundamentally change this paradigm. By hosting the MCP server on a secure, managed platform, a crucial abstraction layer is created. The agent communicates its intent to the remote server, which then securely authenticates and interacts with backend systems using managed credentials and policies. The user's environment is no longer the weak link.
Centralized governance through agent gateways
The true power of remote MCP is realized through centralized "agent gateways" where these servers are registered and managed. This model delivers the essential guardrails that enterprises require.
Instead of granting agents unfettered access, administrators can now allowlist specific, trusted MCP servers, ensuring only approved tools and functionalities are exposed. For example, an organization can connect its digital labor platform to a certified, remote MCP server for a payment provider like PayPal or Stripe. This allows them to benefit from a full range of agentic commerce capabilities without ever exposing raw credentials to the agent or the end-user.
Furthermore, these gateways enable organizations to wrap custom MCP actions with their own business logic and policies. This provides fine-grained control, maintaining strict oversight of how external tools operate within an organization's context. Concerns about proprietary data being used to train external Large Language Models (LLMs) are also mitigated, as platform-hosted MCP servers ensure that sensitive data remains within a secure and trusted boundary.
Scaling up: managing complex toolchains with job-based topics
While gateways provide security, managing a growing ecosystem of dozens or even hundreds of registered MCP tools introduces a new challenge: orchestration. How does an AI agent reliably select the correct sequence of tools for a complex, multi-step task?
The most scalable approach is to add another layer of abstraction: organizing toolchains into "topics" based on the "job to be done." Platforms like Salesforce's Agentforce are pioneering this model. Rather than forcing an agent to discover and assemble a toolchain from scratch, an administrator can pre-define a hierarchical set of tools for a specific business capability.
For example, a topic like "New Customer Onboarding" could be configured to grant the agent access to a specific sequence of MCP servers:
A Salesforce MCP to create the customer Account.
A Stripe MCP to set up a new billing subscription.
A DocuSign MCP to generate and send the service agreement.
A Slack MCP to post a success notification to the sales team.
This job-based approach dramatically simplifies agent logic and enhances governance. The agent only needs to identify the high-level topic, and the platform provides the pre-approved, reliable toolchain. This scales human oversight, improves consistency, and allows administrators to manage permissions at the job level, ensuring agents only have access to the tools required for their specific function.
The right architecture for the job
Choosing between local and remote MCP is not about one being universally "better," but about fitness for purpose. While both have a role, their ideal use cases are distinct.
Choose a local MCP server when:
You are actively building and testing a new integration in an isolated environment.
Your AI agent must access highly sensitive local data that cannot leave your machine.
You require the absolute lowest latency for tools that are also running locally.
Choose a remote MCP server when:
You need to provide tools and data access to web-based AI agents.
You want to provide a simple, zero-setup experience for end-users across a team or organization.
You must ensure consistent, scalable, and secure access to a tool or database for many different users.
You prefer a provider-managed service that handles security, updates, and maintenance.
For most enterprise scenarios—from enabling team-wide agentic workflows to integrating with third-party cloud services—the remote model is the effective and scalable choice.
An emerging marketplace for trusted tools
This shift to a remote, managed architecture is creating a new market category for trusted, interoperable AI tools. Companies are no longer forced to build and secure every integration from scratch. Instead, they can turn to marketplaces that offer pre-vetted, plug-and-play MCP servers from leading technology vendors.
Ecosystems like Salesforce's AgentExchange with planned support from companies like AWS, Box, Cisco, Google Cloud, IBM, Notion, PayPal, Stripe, Teradata, and WRITER exemplify this trend. This ecosystem allows organizations to rapidly and securely assemble sophisticated AI capabilities. For a technical leader, this means the focus can shift from managing infrastructure risk to delivering business value, confidently adopting agentic workflows by connecting to a catalog of trusted, enterprise-ready tools.
The path to production-ready agentic AI
While local MCP servers will remain a vital part of the power-user's toolkit for specific, isolated tasks, the path to secure, scalable, and production-ready agentic AI runs through a remote, platform-hosted architecture. Remote MCP provides the governance and scalability needed to transform agentic AI from a promising concept into a reliable, enterprise-grade toolchain. As this new ecosystem of secure, managed tools matures, open standards like MCP will be instrumental in unlocking the full potential of AI across all industries.