Months after Uber COO said that the company burnt its full AI budget for 2026 in less than four months, CTO Praveen Naga shares update on Agentic AI: Today, 99% of our engineers…

Uber’s push into artificial intelligence (AI) has been a rollercoaster ride. Earlier this year, the ride-hailing giant faced issue over its AI spending, with COO Andrew Macdonald noting the difficulty in justifying the company's “tokenmaxxing”, which is essentially the high cost of large-scale AI usage.

Moreover, CTO Praveen Naga previously revealed that the company had burned through its entire planned budget for Claude Code in less than four months. However, Naga has provided an update, saying Uber has shifted its strategy from simply consuming AI services to systematically embedding “Agentic AI” into the core of its business, achieving a level of adoption where 99% of its engineers now use AI tools.

Uber CTO says company is moving beyond ‘Tokenmaxxing’

To bridge the gap between high AI costs and real-world efficiency, Uber launched a project called “Agentic Pods”.

The goal was to stop treating AI as an expensive experiment and start using it to redesign complex, manual workflows across departments like Finance, Legal and Marketing.Naga said that the company handpicked approximately 30 of its most AI-proficient engineers and paired them directly with domain experts – the employees who actually perform the work. The teams operated on a strict, two-week schedule. The claimed that in just two months, Uber successfully ran 16 of these “Agentic Pods” across 16 different business functions, and noted that the efficiency gains were significant.

Read what Uber CTO Praveen Naga said

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!