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@udaykiran Introduction
AI tools are now embedded in every phase of software development at Uber. More than 70% of pull requests are attributed to local or cloud agents. Engineers have built over 3,600 agent skills across the software development life cycle, and executed more than 30K agent skill executions per day.
At the AI Engineer 2026 conference, we shared our vision for the Software Factory and the building blocks and managed agents we are building across the lifecycle. As we progress on that vision, a growing share of sessions aren’t initiated by humans, but by automated managed agents handling code review, self-healing CI failures, completing E2E PRs with visual validation, triaging on-call alerts, debugging incoming bugs, and handling a variety of code maintenance tasks with human reviews/escalations.
As shown in Figure 1, from February to Aug 2026, weekly active users across all agentic offerings across all our employees (engineers & non-engineers) grew 7x, and weekly agentic requests grew 9.4x. Meanwhile, our total AI spend has relatively stabilized since April due to optimizations across the board.
Since adoption, workload mix, and model upgrades are all continuously changing, isolating our own optimization gains means holding one model fixed, since behavior shifts with every upgrade and model family. We did that from February to July: cost per 1,000 model requests is down almost 34% from its peak, and cost per session is down 52% from its June peak.
This blog walks through how we think about our software factory: the four layers agent sessions run in, the cost equation we use to decompose spend, how we measure each term, and how we optimize those terms across every layer.
All pricing and vendor metrics in this comparison are based on publicly available information, with cost efficiency gains driven by routing our internal Uber workloads more intelligently within standard tier-pricing. While specific cost reductions we measure are unique to our environment and your mileage may vary depending on your codebase, team size, and agent workflows, the methodology of benchmarking real work and optimizing for accuracy and cost is universally applicable.
The Software Factory and Its Cost Equation
Four Layers of Agent Usage
We organize AI usage into four layers, from the most specialized to the most general. As shown in Figure 3, the higher the layer, the more control we have over cost, quality, and model selection.
The Cost Equation
Across any of the layers above, we can decompose the cost of an agentic session into the following terms, which we could measure and optimize independently.
The first two terms represent adoption & engagement, which we want to keep growing across our overall user base, whether users use it interactively or agents handle tasks on their behalf. The three middle terms provide opportunities for optimization: the work the agent does on its own behalf, on top of the request an engineer actually made. That is where most of our effort goes. This includes mechanisms that help agents plan faster, reduce unwanted turns or errors, optimize input tokens, and more.
How We Measure
Below is the full set of metrics we track weekly and monthly that enable us to forecast & plan our efforts short-term and long-term.
Optimization Levers
In the following sections, we detail the key levers we used to optimize each part of the cost equation. Some of these levers affect one or more rows in the cost equation.
Optimizing Price / Token
The vendor sets the token price. We pick which model runs which workload. Across all our managed agents’ layers, we pick the model that’s most Pareto efficient for that workload. For us, Pareto efficient means cost/completed task, output quality, and model reliability.
Benchmark-Driven Model Selection
Model selection happens in four steps, the same for every managed agent we run.
Build a benchmark out of the agent’s real work.
Run the agent on a harness that serves any model, frontier or open-weight, behind one interface.
Move to whatever is Pareto optimal, and keep moving. The frontier shifts every few weeks.
Looking ahead, we continually refine our workload performance by leveraging aggregated insights from our managed agents to test and deploy various model routing strategies.
For example, we use uReview, which handles AI code review for all pull requests. We built its benchmark from real pull requests with known bugs and graded them easy, medium, and hard. We score precision, recall, and F1 against those bugs, plus cost per review, latency, timeouts, and noise. As shown in Figure 5, switching models improved our F1 while dramatically reducing cost/PR. In the figure, the dashed line is the Pareto frontier. Everything below and left of it is beaten by something cheaper or better.
Using thousands of real-world PRs across our large monorepos, we internally also have an Uber SWE Benchmark that runs frontier and open-weight models across differe…
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