Day 1 · Recorded 6 May 2026
Cloudflare's path from assisted coding to delegated engineering
If your AI rollout is stuck with a few early adopters, Cloudflare's story is the useful counterexample. Rajesh Bhatia walks through the platform primitives, identity controls, context systems, and code-review gates that helped Cloudflare move from assisted coding to delegated engineering across 93% of R&D.
Rajesh Bhatia, Senior Director of Engineering, Cloudflare
What's in this session
Enterprise AI adoption does not happen because a team buys better coding tools. At Cloudflare, the useful work came from building a platform layer around AI: context, trust, controls, visibility, and low-friction defaults that made the tools relevant to real work.
In this interview, Rajesh Bhatia walks through the stack behind that adoption: AI Gateway for routing, controls, and visibility; Access for identity; Backstage and Agents.md for service context; MCP infrastructure; one-command setup and shared config; and a multi-agent code reviewer tied to machine-readable engineering standards.
For developer productivity and platform leaders, the takeaway is a practical adoption playbook. The shift from assisted coding to delegated engineering depends on context systems, cost attribution, evals, objective review gates, and defaults that let engineers trust agents with more consequential work.
Inside the recording
- 00:00 Cloudflare's AI engineering stack in practice
Rajesh explains the platform work behind AI use across thousands of Cloudflare employees. - 03:00 AI Gateway, Access, Backstage, and Agents.md
Cloudflare reuses its own primitives for routing, identity, context, and internal controls. - 06:00 Getting to 93% R&D adoption
Low-friction defaults, one-command setup, shared config, and real work drove the inflection. - 17:30 Backstage as the system map for agents
Ownership, dependencies, service boundaries, and conventions reduce how much models have to guess. - 21:00 Measuring value beyond PR throughput
Requests and tokens show usage, but feature velocity and safe change flow are the harder metric. - 27:00 AI code review as an objective gate
Engineering codex rules turn subjective model comments into findings tied to real standards.
More sessions on agent infrastructure
- Building Minions: agents on a 30-million-line codebase — Alistair Gray, Stripe
- Building a company-internal background agent system — Cole Murray, Open Inspect
- Background Agents for Genomics: Operating Across Science and Cloud Infrastructure — Xiucheng Quek, Genentech, Inc