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Uber Shares What Happens When 1.500 AI Agents Hit Production
Uber's implementation of a centralized MCP gateway and registry to manage and secure the deployment of over 1,500 AI agents.
TL;DR
Uber is managing the deployment of over 1,500 AI agents by implementing a centralized MCP gateway and registry. This infrastructure addresses critical challenges in security, tool discovery, and development standardization across their engineering organization.
Uber's engineering organization, comprising over 5,000 engineers, has integrated AI into their monthly workflows, with more than 1,500 active agents performing over 60,000 executions per week. However, scaling this level of automation presented significant operational risks, most notably the potential for agents to cause rapid, large-scale accidental damage. The primary challenges identified by Uber's leadership, Meghana Somasundara and Rush Tehrani, were the lack of standardized development practices, which led to duplicated work across 10,000+ internal services; security concerns regarding unauthorized endpoint access and data exposure; and the difficulty of discovering reliable, high-performing MCP (Model Context Protocol) servers. To mitigate these issues, Uber developed a centralized MCP gateway and registry that acts as a control plane, allowing service owners to define and expose endpoints securely. This system ensures all changes pass through security scans and pull requests, providing necessary visibility into call patterns. Additionally, Uber introduced a no-code Agent Builder that allows engineers to pre-select tools and lock parameters, reducing the number of autonomous decisions an agent must make at runtime. This structured approach has proven successful, with their 'Minions' coding agent generating approximately 1,800 code changes weekly. While the scale of Uber's infrastructure is extreme, the principles of centralized governance and standardized tool discovery are highly relevant to any organization scaling agentic workflows.