An AI app should not put conversations, artifacts, memory, retrieval documents, locks, and eval traces into one store. D1 fits queryable product data, R2 fits large files and artifacts, Durable Objects fit named coordination and per-session state, and Agent Memory / AI Search / Vectorize handle memory and retrieval.
Agent Platform uses a Cloudflare-first architecture: local `npm run dev` runs Node-based simulations, while production maps to Workers + Workers Assets + D1 + KV + R2 + Vectorize + Queues + Workflows + Durable Objects + Workers AI. The Runtime interfaces stay the same (InMemory → Cloudflare implementations), so upper layers migrate without noticing. Deployment requires only `wrangler login` → create resources → fill in IDs → `wrangler secret put` → `wrangler deploy`. CI/CD watches the main branch and runs typecheck + build + dry-run + migration + deploy.