Uber Engineering

Engineering @Uber, from practice to people.
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[Praveen Neppalli @praveenTweets]

Great to host @gdb at @Uber HQ this week. We’ve worked closely with @gdb and the @OpenAI team for several years, and it’s remarkable how much has changed.

The first piece of work we did with ChatGPT years ago was with our support team, tinkering with a 4K context window :-). That’s hard to imagine now.

Now we’re using the latest frontier models to automate engineering workflows at global scale, moving into the era of agentic engineering.

Today we’re expanding agentic capabilities across the whole company. Following a 1,000-person pilot, we’re rolling out ChatGPT Work from @OpenAI for Business to every Uber employee. We’ve seen the shift in engineering, moving from AI helping with individual tasks to completing whole workflows, and we want to bring that same shift to teams across the business.

I’ve spoken before about the ‘AI pods’ we launched earlier this year, pairing AI engineers with teams across Uber to improve how work gets done. One team reduced the time needed for an intense capital allocation process covering 150 cities from 15 hours to 30 minutes. That’s what I love about applied AI: combining the latest technology with the expertise of people on the ground to completely rethink what’s possible. Now we want to do that at scale.

Uber has always been a company of builders, and every day millions of people around the world rely on what we create. I can’t wait to see what the team builds.

[Uber Engineering @UberEng]

Over 1 million lines of dead code removed. 🧹

Meet AutoClean, Uber's AI-powered system that's cleaned up 16,000+ stale feature flags using AI agents, deterministic code transformations, and validation. https://t.co/NMJqwrYnhF

[Uber Engineering @UberEng]

At Uber's scale, serving millions of translations per second makes memory efficiency critical.

We reworked how translations are stored and cached, cutting CPU usage by over 30% and reducing the initial cache memory footprint by over 90%, all while maintaining low latency.

🔗 https://t.co/kHCi2C5oPO

[Uber Engineering @UberEng]

https://t.co/hZjJWYlnye

[Uber Engineering @UberEng]

New research from Uber Engineering 🔬

How do you keep AI agents focused on what they’re actually designed to do and block malicious attacks that attempt to manipulate agents into doing things they weren’t designed to do?

Our researchers explore exactly that with Persona Guardrail, a production-grade defense framework for agentic systems.

Congrats to the team behind the work! 👏

Read the paper: https://t.co/BMRWkAgcGp

[Uber Engineering @UberEng]

And if you missed it, check out the team’s Agentic AI Foundation newsletter-featured blog, “How Uber Protects Against Retry Storms”
https://t.co/xBQ3jcQe8P

[Uber Engineering @UberEng]

What does it take to scale MCP across Uber?

Our latest engineering deep dive traces Uber’s MCP journey. From early, community-driven adoption to building MCP Gateway, a centralized platform designed to make AI-powered developer workflows easier to discover, manage, and scale.

The team dives into how MCP Gateway brings together discovery, control, and proxy layers to connect AI agents with Uber’s existing backend systems.

Featuring work from several authors recently recognized in the @AgenticAIFdn newsletter, the team unpacks the architecture, challenges, and lessons behind building MCP Gateway.

[Uber Engineering @UberEng]

https://t.co/p4OzJicyw8

[Uber Engineering @UberEng]

https://t.co/p4OzJicyw8

[Uber Engineering @UberEng]

What if a node failure didn’t have to ripple across your cluster?

M3DB’s subclustered placement algorithm creates smaller, self-contained failure domains—limiting shard migration impact, enabling safer parallel operations, and making large clusters easier to reason about. ��️
https://t.co/ojBpyzVr8Y

[Uber Engineering @UberEng]

How do you keep ML features consistent at 8M QPS?

We built a feature logging framework that selectively captures the features used at inference for training, reducing training-serving skew without logging trillions of unnecessary rows.

See how it works. ⬇️
https://t.co/v7myHXU04s

[Uber Engineering @UberEng]

9.5M unnecessary requests. Stopped.

Uber’s error ownership system identifies where failures originate so retries happen where they can actually help, not repeatedly across the call chain.

The result: max retry storm radius dropped from up to 25 levels to 3.

https://t.co/cOTuhntRVl

[Uber Engineering @UberEng]

At Uber’s scale, tracing every failure across a complex service mesh isn’t practical.

So we built a system that automatically maps how failures propagate across dependencies—helping us identify hard dependencies and strengthen reliability.

https://t.co/hW54Xg5Otq

[Uber Engineering @UberEng]

⚡ We cut Uber Eats search latency in half.

How? Measure. Identify. Fix. Validate. Repeat.

An AI coding agent helped us find bottlenecks, test fixes, and validate impact—turning optimization into a continuous feedback loop.

See how → https://t.co/N2Cm0yQkm5

[Praveen Neppalli @praveenTweets]

Since I previously shared how we are scaling AI usage so quickly without costs rising at the same rate, there’s been a lot of interest in the engineering details.

So I asked the team to open up the hood and share what we’ve learned.

One thing we’ve always been very good at @Uber is understanding the economics of a system at a deep level. And I’ve always believed that constraints drive innovation. We took the opportunity to turn the cost problem into an engineering problem. We broke AI spend down into its underlying levers and started systematically attacking each one:

• Scaling vendor-neutral managed agents
• Building Uber SWE benchmarks to measure what actually works
• Optimizing prompt caching
• Making tool and MCP usage more efficient
• Grounding trajectories in our context graph
• Building efficient, reusable agent skills
• Giving engineers real-time cost visibility and optimization tips

There’s a lot more work to do in this space: scaling more autonomous agents, dynamic model routing, a self-evolving context graph, and continuously updating efficient agent skills. There's still a tremendous amount to invent here, and we’ll keep sharing what we learn as we build and deploy it at Uber.

This is an incredible time to be an engineer. The economics of software are changing, the way we build is changing, and engineers have an opportunity to help define what comes next.

Read the deep dive here: https://t.co/y1egvGPqWa and let us know what you think.

[Uber Engineering @UberEng]

Across all agentic tools and all employees at Uber, weekly active users have grown 7x and weekly agent requests have grown 9.4x.

Despite that growth, total AI spend has relatively stabilized and cost per token has decreased.

In our latest blog, we break down the architecture and operational choices driving AI efficiency at scale, including better prompt caching, access to 1,000+ internal and third-party MCP servers through a single gateway, and an AI Context Graph spanning 30+ internal systems.

The result? Cost per 1,000 requests for a given frontier model has fallen 34% from its peak, and cost per session is down 52%.

Learn more ⬇️

[Uber Engineering @UberEng]

https://t.co/4E91KTbzDU

[Uber Engineering @UberEng]

https://t.co/4E91KTbzDU

[Uday Kiran Medisetty @udaykiran]

The recording of the @aiDotEngineer World's Fair session from Jun 30, 2026, that @hudaman and I did on Building Blocks for Uber's Software Factory is now live!

We’re moving closer to an autonomous, agent-driven Software Factory at @UberEng. Underneath it is an AI infrastructure stack designed to let agents safely understand our systems, take action, write and validate code, and maintain software at Uber scale. Six building blocks power the factory:

* Model Gateway: PII redaction, safety guardrails, observability supporting 800+ projects and 100M+ requests/day.
* MCP Gateway: Connects agents to thousands of internal APIs and SaaS tools, with token-efficient patterns like Omni MCP, CLI, and code-mode skills.
* Devpods: Pre-provisioned Kubernetes environments where agents can securely build, execute, and test code.
* Skills Marketplace: Skills Marketplace: 3,6K skills with 30K+ executions/day, packaging reusable engineering workflows for agents.
* Context Graph: 100M+ entries across 200+ node and edge types, helping agents discover context with lower token and latency overhead.
* Cortana: Our AI assistant bringing everything together across Slack, CLI, and web, handling 25K+ sessions/day.

We also demoed the full agentic SDLC, taking an idea from Figma → code generation → visual validation → self-healing CI → AI code review → automated code maintenance.

70%+ of Uber PRs originate from local or cloud agents, lines of code per engineer have doubled YoY, and 250+ automated migrations have touched 9M lines of code.

Full video here: https://t.co/q7irSupuAZ

[Uber Engineering @UberEng]

A query can return few records and still scan years of data.

At Uber, we reduced the cost of these “needle in a haystack” export workloads by combining Apache Hudi column stats with predicate-column sorting for selective file pruning.

🔗 Read more: https://t.co/F1XhEIJDgr

[Uber Engineering @UberEng]

Discover the core principles behind Uber's Payments Platform—designed 10 years ago and still powering a business with $217B in annualized gross bookings.

🔗https://t.co/4BfU72VMfq

[Uber Engineering @UberEng]

Big ideas, bold builds, and incredible energy! 🚀 We loved celebrating our interns for #NationalInternDay and seeing their creativity come to life at this year’s intern hackathon. The future of engineering is looking bright! 💪

[Praveen Neppalli @praveenTweets]

Last week, we celebrated our interns in honor of #NationalInternDay with a dedicated day of events, and it was a great reminder of the energy and talent that make our program at @Uber so special.

I had the opportunity to join a panel alongside three former interns who are now software engineers on my team, and saw firsthand the immense creativity at this year's intern hackathon.

Spending some time today with this group left me more convinced than ever that the future of engineering is bright!

@UberEng