AI Developer Tool Strategy at Uber - Uber's AI developer tool strategy focuses on products that directly improve developer workflow, such as writing tests and reviewing code [6] - The strategy emphasizes building crosscutting primitives, foundational AI technologies applicable across multiple solutions [7] - Intentional tech transfer is a cornerstone, identifying and spinning out reusable components to reduce barriers for future problem-solving [8][9] Key Products and Their Impact - Validator, an IDE experience, flags best practices violations and security issues, resulting in thousands of fixed interactions daily [11][16] - Autocover, a test generation tool, leverages domain expert agents and has increased developer platform coverage by approximately 10%, saving an estimated 21,000 developer hours [17][27][28] - Uber has built an internal custom GPT store where you can build chatbots that are steeped in Uber knowledge [29] Technical Learnings - Building super capable domain expert agents yields outsized results due to better context utilization and reduced hallucination [34] - Composing agents with deterministic sub-agents, like the lint agent in Validator, ensures reliable output [36] - Scaling development efforts is achieved by creating and reusing agents across multiple applications, such as the build system agent [37] Strategic Learnings - Encapsulation through well-defined abstractions like Langraph boosts collaboration and allows for horizontal scaling of development [39] - Graphs model interactions effectively, mirroring developer workflows and improving both AI and non-AI experiences [41]