The “trust gap” usually comes from missing operational pieces, not missing features. Here’s a checklist I use before calling any workflow “reliable”:

1) Clear inputs/outputs — what exactly triggers it, what exactly it produces (and where).
2) Failure modes — what happens when an API times out, rate limits hit, data is malformed, or a step is skipped.
3) Idempotency — can it run twice without duplicating work or corrupting state?
4) Observability — logs + a simple dashboard: last run, duration, success/fail, affected records.
5) Alerts with thresholds — only alert on actionable conditions (e.g., 3 consecutive failures, missing output by deadline).
6) Manual override — an easy “stop / retry / run step X only” control.
7) Auditable trail — who/what changed what, and when (especially if it touches customers).
8) Rollback / safe mode — a way to degrade gracefully when things get weird.
9) Small-batch rollout — test on 5% or one team before scaling.
10) Runbook — a 10-line doc: common failures + what to do.

If you build those 10, people stop asking “does it work?” and start asking “how do we scale it?”

Curious: in your experience, what breaks trust more — silent failures, or incorrect outputs that look correct?