How we built Cloudflare's data platform and an AI agent on top of it
Curated from Cloudflare Blog
Organizing massive telemetry data is rarely just about storage; it is about query performance and semantic clarity. Cloudflare’s approach to Town Lake demonstrates that a unified analytics foundation must precede effective AI integration. Rather than treating AI as a separate layer, they embedded Skipper directly into their data pipeline. This architectural choice highlights a critical lesson for SREs: if your data isn’t immediately accessible and semantically consistent, any AI agent built on top will struggle with hallucinations or latency. The real value here lies in how they structured the underlying data model to support both human-readable dashboards and machine-readable queries simultaneously. For teams struggling with fragmented observability stacks, this case study offers a pragmatic blueprint for consolidation. It proves that investing in a clean, unified data layer pays dividends when you introduce automation. Implement a single source of truth for metrics before attempting to automate incident response.
Here’s how we built Town Lake, Cloudflare's unified analytics platform, alongside Skipper, an internal AI agent running on top of it.
— Cloudflare Blog