Define canonical failure behaviour
APIs need predictable error contracts so frontends can distinguish validation errors, retryable failures and hard failures. Canonical payloads are a product-quality feature, not just backend housekeeping.
Add observability before you need it
Request IDs, OpenTelemetry and structured logs make it possible to reconstruct what happened when a user reports a failure. Without them, AI products become very difficult to debug in production.
Treat secrets and capability URLs as sensitive
DDDecks.ai redacts secret-bearing render/share paths from logs and applies security headers, request-size limits and no-store rules. Production hardening is mostly about removing assumptions that were harmless in a prototype.
Protect the product from architecture drift
The backend fails startup if duplicate method/path registrations would silently shadow handlers. Small safeguards like this prevent entire classes of hard-to-find bugs.
Reduce scope when necessary
The fastest route to production is often subtraction. V2 deliberately narrowed the product to the core upload-to-deck lifecycle rather than carrying every experimental surface forward.