Why production AI systems need MCP gateways

We’ve all heard of API gateways, and by now, most people are also familiar with LLM gateways. But what are MCP gateways and what critical problems can they help solve for companies using AI across their production tech stack?

What are MCP gateways and how do they differ from LLM gateways?

An MCP gateway acts as a centralized channel through which an organization’s agents interact with external data sources and tools. All communications flow through this channel, creating a single plane that can handle tool discovery and access, authentication/authorization, and tracing/logging. If an organization has X agents connecting to Y MCP servers, the gateway turns that X*Y mesh of direct, unmanaged connections into a single X+Y hub-and-spoke model where each agent connects once to the gateway and the gateway manages the connections to each MCP server.

MCP gateways differ from LLM gateways in that an LLM gateway manages your connection to an AI model (Claude Opus, Codex) while the MCP gateway manages the connection between an agent you deploy and the tools and data it accesses. LLM gateways cover things like token optimization, rate limiting, and request routing. MCP gateways cover things like tracking which of your agents requested a set of data at what time, and whether a certain agent is allowed to send an email from a service account. Both are required for a well-functioning AI ecosystem, but they each serve different purposes in different parts of the stack.

The need for a proxy layer

The “lethal trifecta”, a term coined by security researcher Simon Willison, outlines the three critical conditions required for an agentic system to be vulnerable to attack. An agent must 1) be able to access sensitive data, 2) have exposure to any form of untrusted content (ex: a public feed, external input, or even untrusted tool definition) and 3) be able to externally communicate. AI agents are powerful because they can take in our natural language instructions and turn them into a set of steps that are actioned upon. However, this ability also makes them extremely vulnerable to attack if guardrails aren’t present. If an agent has access to external content, it can’t independently discern useful content from instructions to invoke data exfiltration. With the right access and communication ability, the agent could then pull sensitive data and expose it to an attacker. The data exfiltration risk isn’t theoretical: in 2025, this attack was actually executed against Github’s MCP server.

Beyond protecting against data exfiltration, a proxy layer also facilitates audit trails, performance improvements, and identity and access management (IAM). Together, these capabilities transform the proxy layer from a simple communication channel to the backbone of a production-grade agentic system, fitted with security, compliance, and performance functionality.

Core MCP gateway capabilities

Tool governance and discovery

Depending on level of closeness to agentic development, employees in your organization may have varying levels of familiarity with what tools are available to them. Because of its hub-and-spoke nature, a centralized gateway enables a centralized registry of tools that can be discovered and connected to immediately. Organizations can vet every new tool and update prior to it entering the registry and enforce shadow MCP detection to detect unapproved tools through existing mobile device management (MDM) software on each device. Without a registry, if a tool’s provider were to come out with an update, any agent connected to that tool may be able download the update without knowing whether it breaks existing dependences or is a trusted version. In addition to native MCP servers, the gateway can also wrap REST APIs, legacy SOAP services, databases, and other internal functions as MCP tools and introduce them to the registry.

Beyond discovery, a gateway also allows for granularity in tool access. Consider a database MCP server that exposes both read and write tools across multiple tables. A business intelligence role requires a different set of tools from an engineering contractor role. While the table scope for the engineering contractor is more limited, they might need write permissions on some tables. On the other hand, the BI role wouldn’t need any write capability, but may need broader read access. Gateways allow organizations to segment a single MCP server's toolset and grant access at the role or individual agent level.

Input/output filtering and sanitization

This is where MCP gateways break the lethal trifecta. Any external data entering an agentic system passes through the gateway prior to any agent reading it. Beyond straightforward sanitization, a robust gateway (like Runlayer’s) enforces MCP-specific threat detection on every call by scanning for tool poisoning, tool shadowing, and command injection that generic prompt-level guardrails won't catch. A similar inspection is performed prior to output. Before an agent's output reaches an external endpoint, the gateway inspects outbound content for data loss risks, matching against known sensitive patterns like credentials and PII and blocking any call going to an untrusted or unapproved endpoint.

Authentication and authorization

When it comes to internal systems and production data, it’s critical to verify both the identity of the requesting entity and that they have the adequate permissions to access a specific resource. For identity verification, MCP gateways integrate with existing IdP providers (Okta, Entra, etc.) so that permission sets can be continuously synced from existing systems and don’t need to be managed separately. Beyond human identity, each agent should also have a verifiable identity so that every tool call can be attributed to a specific agent, not just the user or role behind it.

As AI systems become more sophisticated, so do the attacks on them. Regarding access management, role-based access controls (RBAC) are simply not enough anymore to protect against malicious actors. A production-grade gateway should employ policy-based access controls (PBAC) that consider the full request context at runtime, including tool arguments, request metadata, subject attributes, and network origin. For tool calls that touch particularly sensitive resources, a gateway can also require human approval prior to execution, ensuring a person consciously gives the go ahead on the given action.

Traffic management and scale

As a company’s number of agents and/or tool calls scale, an MCP gateway can also help optimize performance and prevent service degradation. During traffic spikes, the gateway could queue requests to smooth out load and pool connections to prevent backend services from being overwhelmed. It could also handle caching to prevent redundant tool discovery and rate limiting to ensure no one agent or workflow is monopolizing shared resources. Performance issues may not impact development environments, but as a company deploys more agentic workflows to production, managing traffic and resource availability goes from a nice-to-have to a critical piece of functionality.

Observability and auditability

If your organization maintains compliance certifications (SOC 2, HIPAA, ISO 27001, etc.), keeping an accurate record of data access is paramount. Logs construct an auditable trail of every tool call made (with full request/response traces, timestamps, data access records, and requesting entity), making debugging, incident response, and auditing infinitely easier. Because all calls flow through one endpoint, tracing actions happens without blind spots.

Deploying MCP gateways in production

Organizations across a variety of industries have already begun using gateways in their production environments to achieve some or all of the functionality above. Some examples:

• Caching high-demand queries: Multiple individuals across sales and marketing teams at a company need to access quarterly profitability data for automated lead generation workflows. The gateway caches this query to improve response time and cost per request. The gateway also enforces identity verification for both the employees and the agents executing the workflows.
• Seamless compliance: A healthcare startup is is preparing for a HIPAA audit and requires detailed logs of PHI access across the audit period. A single query into their gateway's audit logs allows them to pull every instance of autonomous data access marked with timestamps, identities, and other relevant metadata.

These are two examples of a much broader set of use cases. As agentic systems become a standard part of enterprise infrastructure, the gateway layer will become as foundational as the firewall and control plane for organizations looking to secure their data and scale autonomous operations.