Escaping the Fork: How Meta Modernized WebRTC Across 50+ Use Cases

Meta engineers employ "Trust But Canary" strategy for configuration rollouts at scale, utilizing canary rollouts and progressive rollouts to ensure safe deployments and detect regressions early. Health checks and monitoring signals help Meta's Configurations team catch problems before they spread, while data and AI/machine learning tools significantly reduce alert noise and accelerate error diagnosis. Incident reviews prioritize system improvements over blame, fostering a culture of safety and reliability.

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.