Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.
Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:
How do we bring agentic AI beyond engineering?
Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.
These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.
So we created something called Agentic Pods.
The idea is simple.
We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.
Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.
In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.
The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.
The biggest lesson? The best AI opportunities are rarely visible from the outside.
You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.
We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.
It's exciting times!
I think this second strategy should have been the main strategy highlighted rather than pushed to a reply on the OP.
And of course, if I'm looking at this outside of the realm of Anthropic models I'd consider GPT 5.5 for the Workers (Also really enjoyed testing various 5.6 variants and am excited to consider them in this flow)
Was thinking today how nice it is to process 1.5 billion requests per month and @vercel "just handles" the scale for us. They've really built something special that allows eng teams to focus on building.
The entire Criterion Closet is now available as a website, where you can browse all 1,247 films by walking the shelves, thanks to redditor olievans.
https://t.co/O2b7MwzCZj
You’ve Got Mail 📧
Proud owner of the “Send It�� hat I got from @resend at a conference. Great product, great people, and another great integration available with just a few clicks.
This is a big project my team and I have been working hard for a few months.
Ship complex frontend+backend apps, fluent local development, great DX all around.
Check it out. We are making Vercel the best platform for backends.
You can now run any Dockerfile on Vercel.
# 𝙳𝚘𝚌𝚔𝚎𝚛𝚏𝚒𝚕𝚎.𝚟𝚎𝚛𝚌𝚎𝚕
𝙵𝚁𝙾𝙼 𝚐𝚘𝚕𝚊𝚗𝚐:𝟷.𝟸𝟺
𝙲𝙾𝙿𝚈 . .
𝚁𝚄𝙽 𝚐𝚘 𝚋𝚞𝚒𝚕𝚍 -𝚘 /𝚜𝚎𝚛𝚟𝚎𝚛 .
𝙲𝙼𝙳 ["/𝚜𝚎𝚛𝚟𝚎𝚛"]
https://t.co/xOUMi4zxpD
Vercel Functions can now be up to 5 GB in package size on Fluid compute, a 20x increase from the previous 250 MB limit.
Bring Python data/AI libraries, browser automation, image processing, and bigger backend apps to Vercel. https://t.co/hkQxJ0ASQe
Vercel WebSockets just dropped and the pricing model is the actual crazy part
you don't pay for idle connection time
only pay when your function is actively processing a message
so you can hold 1000 open connections and get billed basically nothing until traffic spikes
it runs on Fluid Compute, same limits as regular functions, https://t.co/i5HOnhdFaZ works with zero config
this is 5 lines of code to deploy a ws server:
import { WebSocketServer } from 'ws'
const wss = new WebSocketServer({ server })
wss.on('connection', ws => ws.on('message', d => ws.send(d)))
export default server
the separate ws infra tax is gone