Trust But Canary: Configuration Safety at Scale

Meta's Real-Time Communication Team solved the "forking trap" in WebRTC by migrating 50+ use cases to a modular architecture built on top of the latest upstream version. They achieved this through a dual-stack architecture, using a shim layer to dispatch calls between the application layer and WebRTC. This resulted in a significant reduction in binary size and improved performance, while enabling safe A/B testing of new upstream releases before rolling them out.

Meta engineers developed a pre-compute engine with a swarm of AI agents to generate 59 concise context files encoding tribal knowledge that aids in navigating a large-scale data processing pipeline. This resulted in AI agents having structured navigation guides for 100% of the code modules (up from 5%) and documenting 50+ non-obvious patterns. The system works with leading models, is model-agnostic, and self-maintaining, with automated jobs periodically validating file paths and updating context.