May 7
Loading video player...
Keynote: Operating MCPs at Enterprise Scale: Uber’s Journey - Meghana Somasundara, Agentic AI Lead & Rush Tehrani, Head of Engineering, Agentic AI Platform, Uber Technologies, Inc. Meghana Somasundara and Rush Tehrani, who lead Uber's agentic AI platform, reveal how they took MCP from a promising protocol to a production system operating across 5,000+ engineers, 10,000+ services, and 1,500+ monthly active agents. This keynote covers the real challenges of running MCP at enterprise scale, including governance, security, tool discovery, and what it took to build the MCP Gateway and Registry that now powers 60,000+ agent executions per week. Topics covered in this talk: Uber's AI Scale - 5,000+ engineers with 90% using AI monthly, 1,500+ active agents, and 60,000+ weekly executions Three Classes of MCP Problems - Development lifecycle fragmentation, security and governance gaps, and discovery and quality challenges MCP Gateway and Registry - The control plane for all MCP interactions at Uber, with config-driven auto-generation of tool definitions from 10,000+ service IDLs Gateway Architecture Deep Dive - How the orchestrator crawls proto and thrift files, uses LLMs to generate MCP descriptions, and serves tools through a unified gateway service Security at Every Layer - Central authorization, PII redaction, periodic code scanning, mutable endpoint blocking, and full observability with metrics and tracing Three Agent Surfaces - Uber Agent Builder (no-code), Uber Agent SDK (code-first for grocery, care, and customer support agents), and coding agents (Claude Code, Cursor, Minions) Minions Background Agent - Uber's internal agent producing 1,800 code changes per week, built on the Claude harness Improving Agent Reliability - Tool selection scoping and parameter overrides to reduce LLM hallucination in tool calls Roadmap: Quality and Discovery - MCP evaluation metrics, SLA tiers, an "omni MCP" tool search capability, and shareable skills with A/B testing This talk is essential for platform engineers, engineering leaders, and anyone building MCP infrastructure at enterprise scale who needs a battle-tested blueprint for governance, security, and tool management. Links & Resources: Uber AI Solutions / Agentic AI Tech Stack: https://www.uber.com/us/en/ai-solutions/the-agentic-ai-tech-stack/ How Uber Uses AI for Development (Pragmatic Engineer): https://newsletter.pragmaticengineer.com/p/how-uber-uses-ai-for-development MCP Dev Summit: https://events.linuxfoundation.org/mcp-dev-summit-north-america/ Agentic AI Foundation (AAIF): https://aaif.io/ Timestamps (approximate, verify before publishing): 00:00 Intro and talk overview 00:34 Uber's AI scale: 5,000 engineers, 90% AI adoption 01:00 The problem: 10,000 services without standardization 01:45 Challenge 1: Development lifecycle fragmentation 02:24 Challenge 2: Security and governance at agent speed 03:09 Challenge 3: Discovery and tool quality 03:29 Solution: MCP Gateway and Registry as control plane 04:05 Third-party vs internal MCP strategy 04:35 Central registry as single source of truth 04:45 Security: authorization, PII redaction, code scanning 05:24 Observability and guardrails 05:40 Gateway architecture deep dive 07:14 Handoff to Rush: MCP consumption at Uber 07:24 Three agent surfaces: Builder, SDK, and coding agents 08:50 Minions: 1,800 code changes per week 09:10 Agent Builder: scoping tools and parameter overrides 10:38 Uber Agent SDK: YAML config and tool selection 11:16 Coding agents: AIFX CLI for Claude Code and Cursor 11:42 Roadmap: eval metrics, SLA tiers, omni MCP tool search 13:15 Skills: shareable recipes with A/B testing 14:06 Closing #MCPGateway #EnterpriseAI #UberEngineering AI Agents at Uber may need to navigate a massive ecosystem of 1000s of services, handle sensitive data, and execute critical business logic. To enable this, we are moving towards an agentic future which leverages a unified Model Context Protocol (MCP) infrastructure to access real-time services. We will share the architectural lessons learned from deploying MCP at an enterprise scale. We will dive into three key technical pillars of our strategy: 1. Protobuf-Driven MCP Servers: How we leverage existing services and protocol buffers to automatically generate MCP servers, providing safe and instant access to 1000s of microservices. 2. Derived Tools and Description Overrides: Why static tool definitions aren't enough for complex workflows. We’ll demonstrate how we allow developers to override and refine MCP tool descriptions via "derived tools," ensuring agents have the specific context needed for particular workflows. 3. Evaluate quality: How we evaluate quality of MCP tools, leveraged in our no-code Agent Builder, which is a tool that democratizes agent building at Uber.