How we’re rethinking work at Cloudflare with Cloudflare OS (opens in new tab)

Cloudflare built Cloudflare OS to let employees use AI agents safely after a sudden increase in demand for production access and automation capabilities. The company’s approach combines AI enablement with strict controls around data access, human accountability, organizational context, and engineering quality. Its experience suggests that successful AI adoption requires meeting both technical and non-technical users where they work.

Why Cloudflare Built Cloudflare OS

• Employees rapidly began using improved AI models and agent-building tools to create internal applications.
• One sales employee requested production access to roughly a dozen systems and administrative deployment permissions for an AI-built “SuperApp.”
• Cloudflare needed to enable experimentation without exposing internal systems, company data, or customer data.
• The resulting platform combines existing products such as Workers and Access with custom services developed for internal AI workflows.

Principles for AI Adoption

• Start with jobs to be done: Teams should identify customer-related pain points, bottlenecks, or missed opportunities before selecting an AI tool.
• Give everyone access to AI capabilities: AI interfaces should not be limited to developers using terminals, code editors, and repositories.
• Keep humans accountable: Employees remain responsible for defining quality, testing outputs, and owning the workflows and agents they deploy.
• Prioritize organizational context: Cloudflare-specific knowledge and canonical internal guidance matter more than simply choosing the most powerful model.
• Never expand permissions through AI: AI tools and agents must inherit users’ existing access restrictions and receive only the permissions required for their tasks. Shared agents must respect each recipient’s permissions rather than the deployer’s.

Engineering Guardrails with the Cloudflare Engineering Codex

• Cloudflare created the Engineering Codex as an authoritative, opinionated guide to engineering practices.
• Unlike policies, which define what engineers cannot do, the Codex describes what they should do.
• Domain owners are responsible for defining quality standards across the codebase.
• AI agents use the Codex throughout the software development lifecycle:
  • Planning work
  • Reviewing merge requests
  • Evaluating technical designs before implementation
  • Reviewing incident reports
• Over four months, these agents identified nearly 250,000 potential issues, blocked 16,000 merges, and caught architectural problems in almost 600 designs.
• Cloudflare is now focusing on helping engineers create evaluation loops for assessing the work produced by their agents.

Rethinking AI Tools for Non-Engineers

• Cloudflare initially gave non-engineering employees developer-oriented tools with more approachable interfaces.
• This approach worked poorly for knowledge workers who create one-off deliverables and interact with many systems of record.
• Code-focused harnesses encouraged excessive “vibe-coded” applications, often without a clear problem to solve.
• Cloudflare then began working backward from users’ actual needs and introduced the idea of a “magic AI email bot” to which employees could delegate unwanted work. The supplied excerpt ends before describing how that system worked.

Cloudflare’s experience recommends pairing broad AI access with strong identity, permission, context, and accountability systems. Organizations should design tools around real jobs to be done—not simply distribute coding agents—and provide interfaces suited to both engineers and non-engineers.