Engineering @Uber, from practice to people. https://xitter.twitterapi.xyz/t.co/iIenzuSNxH ✨
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Every tool an AI agent can use expands its capabilities and its attack surface. Even an authorized action can take an agent somewhere the user never intended.
Uber engineers are building protections across the agent lifecycle to keep actions aligned with user intent and session context. Making those controls effective in production means balancing security with latency, autonomy, and legitimate tasks.
See what we learned about designing and deploying agent security:
More than 200,000 CPU cores power Uber’s data lake ingestion. At that scale, every efficiency gain counts.
Making better use of that compute means examining how Spark executes queries, moves data, and writes to growing tables. Accelerating one stage can leave other bottlenecks untouched.
Uber engineers are improving production workloads with native vectorized execution and evaluating new ways to accelerate write-heavy pipelines.
Explore the engineering choices behind those efforts, where the benchmarks showed gains, and what remains to be optimized.
Technical talks, fresh perspectives, and a community ready to connect. Our mini GHC day at Uber had plenty to celebrate!
After the cancellation of GHC, the Uber community rallied to talk engineering and have some fun. We were especially excited to welcome @EricaLockheimer from HumanizeHer to share her experience and perspective.
Thanks to everyone who took the stage and joined the conversations!
One shared codebase. Multiple teams. A display ads deployment that could force unrelated rollbacks.
As Uber’s ads marketplace grew, teams needed independent deployments without splitting the platform into separate ad servers.
See how Uber Engineering used isolated production fleets to reduce deployment coupling, and why runtime failure isolation wasn’t enough: uber.com/us/en/blog/designin…
Uber Engineering retweeted
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.
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.
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.
🔗 uber.com/us/en/blog/translat…
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: arxiv.org/abs/2610.03434
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.
And if you missed it, check out the team’s Agentic AI Foundation newsletter-featured blog, “How Uber Protects Against Retry Storms”
uber.com/us/en/blog/protecti…
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. ⬇️
uber.com/us/en/blog/from-cha…
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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. ⬇️
uber.com/us/en/blog/taming-m…
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.
uber.com/us/en/blog/protecti…
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.
uber.com/us/en/blog/automate…
⚡ 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 → uber.com/us/en/blog/uber-eat…
