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The Pragmatic Engineer · 4 min · Oct 14, 2026

Wisdom makes employee-reported bugs easier to reproduce

Uday Kiran Medisetty

Distinguished Engineer, Uber

Leads Uber's agentic AI and software factory work; joined in 2015 and rebuilt its fulfillment platform.

  • Gergely OroszHost · Host, The Pragmatic Engineer

    Spent four years at Uber heading mobile, web and backend teams; shipped part of its payments system.

About this segment

Employee reports capture screen history, logs, network requests, and flags; Debug Assist uses that context to find a likely cause. Engineers can replay the same network responses on a test device, then hand a fix to Minion.

From “Uber’s AI infra, from the inside”, Oct 14, 2026. Watch the full episode

“we know that the set of screens the user has gone through because we have that in the logs”
Uday Kiran Medisettyat 1:18:52 · The Pragmatic Engineer1:18:52

Takeaways

  1. Bug reports include the user’s screen history and app telemetry.
  2. Debug Assist uses evidence to identify a likely root cause.
  3. Engineers can replay the reported network responses on a test device.

In this episode

Full episode
  1. 16:03Uber reused cloud workspaces to give agents ready-to-build repos3:18
  2. 29:31Uber generated MCP tools from service contracts5:05
  3. 1:06:31Production replay lets agents validate fixes against real traffic6:18
  4. 38:34Uber’s context graph grounds Cortana in engineering relationships6:21
  5. 50:24Minion handles about 15% of Uber’s production PRs5:22
  6. 55:25Uber still requires human review on every PR5:34
  7. 1:17:44Wisdom makes employee-reported bugs easier to reproduce4:03

  8. 1:23:03Uber is shifting from diff counts to feature velocity7:40Up next

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 1:17:55

    So that class of PRs can be onboarded to this automated manage loop. This is nice. I like it. And then finally, I heard about wisdom. What is wisdom? Oh, yeah. So we use wisdom because all of our employees dog fooder products. So if you're using a rider app or earner app, and if you see any bug, then you can, from the app, report that bug. we now have all kinds of ai stuff where like it will automatically route it to the right team based on the stuff and then when you report a bug because it's from the employee from the employee like beta apps we can capture a whole bunch of logs and telemetry around that that makes it really easy for the team owning that feature to fix it so like in this case it's like an internal feature it's essentially like if you want to test any feature in a different country you can mock the

  2. 1:18:51

    location on the device so if someone reported um that that that issue the first thing is okay now how do you reproduce that before it is it used to be a huge problem to reproduce so now we know that the set of screens the user has gone through because we have that in the logs and then we can give the engineers like this is the reproduction steps and this is the expected behavior and this is the observed behavior and so that we can boil down to the issue quickly and all of the telemetry that we collected from the app you can see all the logs in one place so like whether it's analytics network requests all kinds of feature flags so that also gives a lot of context and now now that we have all of this context we built a managed agent called debug assist that looks all of this looks at the code base and it will try to pinpoint what is the root

  3. 1:19:43

    cause and like what is the impact evidence timeline around that um now i once i think okay this is the right right root cause i can hand off this to minion that we talked about yeah hey like this looks good like why don't you create a pr for this minion will take all of that context that we have it creates a pr it will attach that pr to the jira task the jira task also has the root cause analysis so anyone who's working they know the root cause analysis if someone updated the root cause on the comments or like create a new pr again all of that is data that we can then figure out okay how do we improve the debug assist how do we improve minion we didn't get the right rca or we didn't get the right pr from the rca like both we'll be able to continuously improve and then so do i understand correctly that you can capture and reproduce bugs so much easier right yeah and

  4. 1:20:36

    we also added ability to because we for the employee reported bugs we have all the logs i can taste take my test device and i ask i can ask it to simulate what that employee has experienced so then i once i give the bug uid we can essentially serve the same network uh responses that we served to the person who filed so that i can experience the same issue in my test device app too. So that way, okay, then once you fix it, then I can see, okay, this actually got fixed. And then it makes it really easy to reproduce.

  5. 1:21:13

    I love the recording of the thing because that's I mean, so important, right? Like it's hard enough to do even on the web, you need to build custom tools for yourself if you want to do a website. But it's awesome that you did it. Yeah, I think this helps a lot like then in reproducibility and like getting to the right root cause. So you built all of these systems and especially last year it seems so many brand new systems that are just doing