Uber’s AI infra, from the inside - The Pragmatic Engineer | Lyssna här

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What do developer tools look like inside of Uber, today? They are very different from what you would see at most places. For example, can you guess what service a system called DevPods, Code Inbox, Slate or Wisdom would be providing to engineers?

Today’s episode is a deepdive into the day-to-day tools of devs at Uber, our tour guide being on of Uber’s longest-tenured engineers: Uday Kiran Medisetty. Uday is one of the few Distinguished Engineers at the company, and leads much of the work on agentic AI, today.

With Uday, we talk through how Uber’s developer infrastructure prepared it for AI agents. We look at the systems Uber built before generative AI and the new tools that connect agents to its code, internal services, and engineering knowledge. Uday explains how engineers use background agents, why tools like Slate make development (and bugfixing!) more efficient, and how code review is changing. We also cover Uber’s approach to token costs and how it measures productivity as the engineer’s role changes.

Timestamps

00:00 Intro

02:48 Uday’s early days at Uber

05:40 Uber’s move to microservices

07:55 Uber’s engineering team, then and now

09:05 Developer tooling before AI

16:03 DevPods

19:28 Building for AI agents

29:27 MCP gateway

34:37 Agent Skills repo

38:46 The context graph and Cortana

47:54 PRD and ERD tool