Summary
You're now able to recognize the failure patterns that emerge when a custom Dataverse solution like the equipment-provisioning system moves from prototype to production. You can match each pattern to a targeted fix, instead of reacting to an outage after it happens.
What you learned
- Service protection limits, optimistic concurrency, change sets, and bulk operation messages each address a distinct failure pattern: throttling, stale writes, partial transactions, or high-volume same-table updates. Matching the tool to the pattern prevents one fix from masking a different problem.
- Dataverse requires an OAuth 2.0 token from Microsoft Entra ID for every Web API call. Distinguishing an Entra-level authentication failure from a Dataverse-level authorization failure (a missing application user or an insufficient security role) points you to the right fix. Re-acquiring a token was never the real problem.
- The asynchronous service and preview background operations both hand off long-running work so a caller doesn't wait for it. Choosing between an asynchronous plug-in step and a background custom API depends on the nature of the work. Does it react to something that already happened, or does it run as a discrete, on-demand action a client tracks to completion?