Submission, Pending State & Server Errors Handle asynchronous failures without losing user input
Phase 21 - Forms with React Hook Form + Zod Volume 06 - Professional Frontend
Client validation is only the first failure layer Form field error - fix locally
Root server error setError("root.server", { type: "server", message: "Unable to save this task.", });
Field-level server error If the API returns a structured error that clearly belongs to one field, map it back with setError("fieldName", ...). Do not guess based on message text. Prefer stable API error codes or structured validation details.
Keep the form after failure Do not erase the form because the server returned 409 or the network disconnected. Preserve the user's work and let them correct or retry.
A disabled submit button prevents accidental double clicks in the browser, but it is not a distributed idempotency guarantee. Payments and other sensitive operations may need a server-side idempotency strategy later.
Phase 13 gave TaskFlow stable error codes and request IDs. Phase 21 consumes that contract rather than creating a second frontend-only error language.
Submission Pending Server Errors and Duplicate Submits matters because it changes a real boundary in TaskFlow. The goal is to understand the inputs, owner, observable output, and failure modes before adding more abstraction.
Restate the goal of “Submission Pending Server Errors and Duplicate Submits” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: failure → typed/domain error → exception mapping → HTTP envelope → client handling. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Create/edit task forms with shared field rules, accessible errors, async submission, server-error mapping, and reset behavior.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
{
"error": {
"code": "TASK_NOT_FOUND",
"message": "Task was not found",
"requestId": "req_..."
}
}Do not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Without copying the final source, create a second small scenario that applies “Submission Pending Server Errors and Duplicate Submits” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Register basic fields and observe dirty/touched/submitting state intentionally.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Showing only generic toast instead of actionable field errors
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.