One of the most important answers a retention AI agent can give is:I don't have enough evidence to act.
A retention agent can complete its task and still put the wrong customer into the wrong flow. It might select the wrong audience, write an unsupported profile property, or recommend a message that does not fit the customer's current situation.
Consider a customer who has not purchased in 180 days. That may make them eligible for a winback flow, but it does not tell us whether they have returned to the store, what they are browsing, whether another flow is already contacting them, or whether there is enough evidence to change the message they receive.
Retention AI
Eligible?180 days since the last order.Enough evidence to act?
- Returned to the store
- Recent browsing activity
- Another flow already active
- Enough customer context
The agent should be able to say: this customer qualifies, this one does not, and there is not enough information to decide on this one.
Before an agent touches customer work, the team should define one measurable outcome, the customer states in which it can act, the evidence it can use, and what happens when that evidence is incomplete.
The same discipline should apply when an agent writes data to a customer profile. Teams should be able to understand where a classification came from, how confident the system was, and how an incorrect result can be corrected.
Klaviyo's Customer Agent shows that some of these controls are beginning to appear through testing, guardrails, audit trails, and human handoffs.
The best retention agent will not always produce another message.
Sometimes the correct answer is that there is not enough evidence to act.
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Retention AITags