Cloudflare shares how they built their internal AI engineering stack using their own products over 11 months. The stack includes Cloudflare Access for zero-trust auth, AI Gateway for centralized LLM routing (20M+ requests/month, 241B tokens), Workers AI for on-platform inference, MCP Server Portals aggregating 13 servers with 182+ tools, an AI Code Reviewer integrated into CI/CD, and an Engineering Codex for standards enforcement. A proxy Worker pattern centralizes all LLM traffic, enabling per-user attribution, anonymous tracking, and zero API keys on developer machines. AGENTS.md files generated across 3,900 repos give coding agents structured repo context. The result: 93% R&D adoption, merge request volume nearly doubling quarter-over-quarter, and a security agent processing 7B tokens/day on Workers AI at 77% lower cost than proprietary models.
Table of contents
The architecture at a glanceAct 1: The platform layerAct 2: The knowledge layerAct 3: The enforcement layerThe scoreboardWhat's next: background agentsStart buildingSort: