A practical framework for evaluating source integrity, worker confirmation, permissions and data handling in a frontline conversational layer.
Start with the job the product is allowed to do
A conversational layer may retrieve approved information, prepare a structured record or route a request. Each job should have explicit permissions, clear boundaries and a defined destination.
Avoid evaluating a broad idea of “AI access”. Evaluate the exact sources, actions and workflows enabled for the deployment.
Source integrity for answers
- Which approved repositories can the product search?
- Does the answer show the source, section and revision?
- How does it behave when the source is missing, contradictory or out of date?
- Can the worker distinguish approved text from a generated explanation?
Worker control for records
When the product prepares a report in a worker’s name, the worker should be able to review what was understood, correct it and confirm it before the record is committed.
The system should also make the handoff visible: saved as a draft, submitted, routed for review or unable to reach the destination.
Permissions, data lifecycle and auditability
- Use the identity and permissions appropriate to the worker and task.
- Define what data is stored, where it is processed and how long it is retained.
- Keep an audit trail of sources, confirmations and system handoffs.
- Document which decisions remain with supervisors or qualified professionals.
Use the narrower claim
Certifications, hosting models and performance claims can change. They should appear publicly only when they are current and approved for publication.
Trust is strongest when the product is precise about what it knows, what it did and what still requires human judgement.