Credits and refunds
Verify the reason and user-stated instruction before a support agent changes a financial record.
action → issue_creditMemory for support agents
A support agent may remember why a customer contacted you. Before it issues a credit, changes a plan, or closes a case, it should prove which memory supports that action.
The gap
“The customer sounds frustrated” can help shape a reply. It should not be treated like “the customer asked to cancel.” ContextDB keeps source and confidence attached so policy can tell the difference.
# good context, not action authority { "content": "Customer may want to cancel", "source": "agent_inferred", "confidence": 0.62 } → useful for conversation, held from action # direct customer instruction { "content": "Cancel at the end of this cycle", "source": "user_stated", "confidence": 0.98 } → evaluate against cancellation policy
Consequential support actions
Verify the reason and user-stated instruction before a support agent changes a financial record.
action → issue_creditDistinguish a request, a historical preference, and an agent inference before mutating account state.
action → change_planKeep the evidence that justified an automated resolution so operators can review what happened later.
decision → reason + evidence IDsKeep your system of record
Your CRM, billing system, help desk, or database remains authoritative for account state. ContextDB stores the remembered evidence and the policy decision that your support host uses before a mutation.
help desk / CRM / billing customer and account records ▲ │ host mutation │ support agent host ▲ │ act / ask / abstain │ ContextDB memories + evidence + decision record
Questions
No. Your team defines business rules and host enforcement. ContextDB evaluates remembered evidence under the active trust policy.
No. It complements those systems by tracking what an agent remembers and why that memory was or was not eligible to drive an action.
The safe result is ask or abstain. The host should request a specific confirmation or route the case to a person.
Model the source, run action recall, and make the host respect the result.