Bonam Bobby

Fair point to raise. In my setup the model only sees the ambiguous remainder, and what it sees depends on how critical the business data is. Its output goes to a person to approve instead of acting on its own, so the approver is the check and I don’t run a separate canary there. If a provider changes behavior, I update the prompt or interface on my side.

Thanks, and the way you scope client work is the version I’d want to copy. Naming each approval gate and its owner in the scope fixes a failure that process advice usually misses: a gate with no named person quietly turns into a rubber stamp. One addition from my side. Naming an owner covers accountability, but not attention. For high-impact gates I’d also look at structure: a second approver, a cooling-off delay on irreversible actions, and the occasional seeded known-bad item to check the approver is really looking. Curious how you handle it when a gate owner is out or leaves. Do you name a backup in the scope?

Good question, and the honest answer is that it depends on the job, and the design should say which. When the timeout fires, that run has already stopped, so a late approval can’t resume it. For anything where the inputs age (market data, feeds, row counts), I’d have the late approval start a fresh run with fresh inputs. Approving evidence that is now stale is the riskier choice. For jobs with a snapshotted, versioned input, a resumable run with reminders and escalation before the timeout can make sense. The point is to decide it per workflow and write it down. My broader idea is the same either way: use AI to build custom tools for repeated work, so the work still runs when the AI doesn’t. A good example is stock analysis. People reuse one prompt and swap the ticker. Build the analysis once as a tool, and each new ticker costs no model call. I did that with stockverdicts.com. Keep the model for the ambiguous parts.