Know what you are handing over.

Name the systems involved, the data needed, who can see it, and what happens when the engagement ends. Review the data-processing agreement before work starts.

Put it into practiceWho can grant access, and who can revoke it?

Models, providers and prices change. Separate your business knowledge and workflow rules from the model so a change does not require starting over.

Put it into practiceWhat would it take to change providers?

A useful agent does not need access to everything. Decide what it can read, draft and change, then test the boundaries before it handles live work.

Put it into practiceWhat is the most consequential action this system can take?

An audit examines agreed evidence. A live workflow may need an ongoing connection. Make that change in responsibility explicit rather than assuming the audit permission covers it.

Put it into practiceWhat stays connected after launch, and why?

Drafts, internal reports and review queues make mistakes easier to spot. Agree which decisions stay with a person before introducing customer-facing automation.

Put it into practiceWho reviews exceptions before they reach a customer?

A convincing voice demo is not evidence that a phone workflow is ready. Test real conversations, handoffs and failure cases. Text may be a better first step.

Put it into practiceCan the team inspect and correct the output?

People need a defined task, usable information and someone accountable for the result. Start with one recurring workflow instead of asking everyone to figure AI out alone.

Put it into practiceWhich weekly task will change, and who owns that change?

Sometimes an existing tool and a clear working routine are enough. A custom build should earn its place through the work it removes and the responsibility it can safely take on.

Put it into practiceCould a simpler setup produce the same useful result?