Human approval boundaries
Protected commitments — payments, cancellations, anything sensitive — stay behind explicit human approval and policy boundaries. No agent grants itself permission to cross that line.
Under the hood of a verified outcome.
This page describes the design of my own internal automation system. It has not been deployed for external clients.
A representative execution path
REQUEST→AUTHORIZE→EXECUTE→EVIDENCE→VERIFY→OUTCOME
Failures and exceptions are surfaced for repair or human escalation — not silently marked complete.
Evidence-backed execution
In my system, a task is only accepted as complete when a separate record confirms it happened.
Independent verification
Execution, challenge and verification can be separated across specialized AI roles, rather than a single model grading its own work.
Execution state & auditability
Every execution is tracked — status, duration, failures and cost — instead of operating as a black box you have to take on faith.
Failure handling
Failures and exceptions are surfaced for repair or human escalation. Nothing is silently marked complete when it isn't.
Duplicate prevention
Retries and repeated events are designed not to create duplicate business actions — the same request should not become two bookings or two charges.
The same discipline sits behind the AI front desk.
Nothing reaches your customer as a commitment until your staff approve it.