Uber Deploys MCP Gateway, Revealing a Blueprint for Enterprise AI Governance

Uber has deployed its Model Context Protocol (MCP) Gateway, a platform designed to manage AI agent interactions and provide a blueprint for enterprise AI governance. This system utilizes a dual-plane architecture and integrated security to ensure accountability and scalability across thousands of internal services.

Sarah Chen

October 3, 2026 · 4 min read

Uber has deployed a new platform to manage how its artificial intelligence agents interact with thousands of internal services, offering a potential blueprint for other large enterprises grappling with AI governance. The Model Context Protocol (MCP) Gateway is now operating at production scale, serving as a centralized control surface for over 5,000 tools running on more than 800 servers. According to Uber's engineering team, the system was built to solve the "operational complexity, security risk, and developer friction" that arose from ad-hoc AI integrations across the company's vast microservice architecture.

Before implementing the gateway, Uber faced a challenge common in large-scale AI adoption: a proliferation of integrations that were difficult to govern. With thousands of backend services using different protocols like HTTP, gRPC, and TChannel, allowing AI agents to interact with them directly created fragmented, invisible data flows and security risks. An analysis from Codingscape highlighted that without a centralized strategy, companies can end up with "no oversight, no cost visibility, and no idea what data is leaving the building." Uber's solution was to create a unified architecture to standardize how AI agents discover and use tools, ensuring all interactions are secure and observable.

Dual-Plane Architecture: Separating Governance from Execution

The core of Uber's solution is a dual-plane architecture that separates governance from execution. This design consists of a control plane, the MCP Registry, and a data plane, the Proxy Gateway. This separation allows Uber to enforce company-wide policies and manage tool definitions centrally without creating a bottleneck for the high volume of live requests handled by the agents. The Agentic AI Foundation described this approach as a "control plane," noting that Uber adopted it not for elegance, but because it was a practical solution to the problem of "agents that move faster than the humans trying to govern them."

The control plane, or MCP Registry, acts as the system's brain. It is responsible for cataloging all available tools, managing access policies, and handling agent registration. Service owners can define which of their APIs are exposed as tools, but the process is managed centrally through code, with changes submitted as pull requests that undergo security scanning before deployment. The data plane, or Proxy Gateway, is the workhorse. It serves as the orchestration and routing layer that handles all live MCP interactions, translating agent requests into the appropriate backend protocol and directing them to the correct service.

Uber's MCP Gateway: Architectural Components and Functions

To manage the complexity of AI agent interactions at scale, Uber's MCP Gateway is built on a dual-plane architecture that separates governance from live operations. The system's key components each have a distinct role in ensuring that agent actions are discoverable, secure, and auditable. The following table breaks down these components and their primary functions based on Uber's design.

An explanatory model of the MCP Gateway's components, their architectural plane, and their primary functions in managing AI agent interactions.
Component	Architectural Plane	Primary Function
MCP Registry	Control Plane	Provides centralized governance. Manages the catalog of all available AI tools, defines access policies, and handles agent registration. All changes are managed as code and subject to security reviews.
Proxy Gateway	Data Plane	Handles live request execution. It routes agent calls to the correct backend service, translates between different protocols (e.g., HTTP, gRPC), and abstracts the complexity of the underlying service mesh from the agent.
Security Token Service (STS)	Security Layer	Issues short-lived, single-hop security tokens (JWTs) for each agent action. This enables low-latency, fine-grained authorization without significant performance overhead.
Actor Chain Propagation	Security Mechanism	Ensures end-to-end accountability by embedding a verifiable lineage in every request. This chain traces an action from the originating user, through any intermediate agents, to the final tool invocation.