How to Build a Review-Request AI Agent That Runs on Autopilot

A review request AI agent takes the whole reputation chore off your plate except the one part that should always stay human. It figures out which customers to ask, personalizes each message to the actual job, times the ask well, and stacks the drafts up for a one-glance approval -- then, once you approve, they go out. 'Autopilot' here means the thinking runs itself, not the sending.

That distinction is the point of this guide, so I will be blunt about it up front: a review request is a message to a customer, which makes it an external send, and on Deelo external sends stay approval-required under the high-risk floor even at full autonomy. No autonomy level in the builder lets it blast reviews unsupervised, and that guardrail is the feature. If you want the simpler trigger-based version -- a rule that fires an ask after every job -- how to set up automated review requests after every job covers that; this guide builds the smarter agent that decides and personalizes.

Be precise about what is automated, because it is most of the work. The agent automates the decision -- who earned an ask and who should be skipped. It automates the timing -- the right number of days after a completed job, not the same instant for everyone. It automates the personalization -- a message that references the actual service, not a generic template. And it automates the tracking -- who has been asked, who responded, who to follow up.

What it does not automate is the send itself, and that is deliberate. Every outbound review request lands behind human approval. So the agent does the ninety-five percent that is tedious and leaves you the five percent that should never be hands-off: the final glance before something goes to a customer. That is a far cry from a dumb blast, and it is more useful than one, because the thinking is done for you and the judgment stays yours.

The value of a review agent is mostly in the who, so write that carefully. Tell it the signals of a good moment to ask: a completed job, a fulfilled order, a positive interaction. Tell it who to skip with equal care -- anyone who complained, anyone with an open support issue, anyone you already asked recently. Nothing tanks a review score like asking an unhappy customer for a public rating, and a good agent's first job is to not do that.

This is exactly the kind of judgment a simple trigger-automation cannot make -- a rule that fires 'after every job' asks everyone, including the customer whose job went sideways. The agent reads the context and decides. Give it the criteria in plain English, including the gray cases, and it will build you a list worth sending to.

Grant the agent CRM so it can read customers, their history, and job or order status to decide who to ask, and Marketing so it can prepare the requests and track outcomes. Set the grid so it can do all the preparation freely: read on customers and jobs -- allow; write on internal notes and tracking (marking who has been asked) -- allow, since that is internal record-keeping, not an outbound message.

The agent needs full run of the decide-and-draft work, so give it that. Reading context, scoring who deserves an ask, and composing personalized drafts are all safe, reversible, internal actions -- there is no reason to gate them. The gate belongs on exactly one verb, and that is the next step.

Here is the one setting that does not move: send stays on allow-with-approval, permanently. A review request is a message going out to a customer's inbox or phone, and every external send on Deelo is held behind human approval by the high-risk floor -- even at full autonomy, even after months of the agent running clean. No autonomy level in the builder lets it switch to send-freely, and you would not want it to. This is the setting that guarantees a review ask never lands on the customer who just complained, never goes to the wrong contact, and never fires in a tone-deaf moment.

The agent does all the work up to the send: it decides who earned the ask, waits the right number of days, writes each message personalized to the actual job, and stacks the drafts up for you. Your job shrinks to a single glance -- approve the batch, and they go. That glance is the feature, not the friction. It is the difference between a system you trust running every day and a mass-mailer you have to babysit, and it is why you can hand this agent your customer list without worrying what it will do with it.

A review agent works best on a rhythm, so schedule it. Set it to run daily or a few times a week: each run, it reviews recently completed jobs, decides who to ask, drafts the personalized requests, and reports back in its own chat thread with the batch ready for your approval. You are not triggering it by hand or remembering to ask -- it surfaces the drafts, you glance and approve.

Scheduling a specific agent like this has its own mechanics -- recurring versus one-time, and how the report-back works -- covered in how to schedule AI agent tasks. The key thing for a review agent: because it runs unattended, the approval floor matters even more, and it holds. The agent prepares overnight; the sends wait for you. You approve a reviewed batch in the morning instead of writing requests from scratch.

The ask is only half the job; the replies are the other half. Brief the agent to triage what comes back. A customer who leaves a glowing review might warrant a thank-you -- drafted for your approval, since replying is another external send. A customer who responds unhappily should be escalated to a human immediately, with the context summarized, not handled by an agent -- a bad moment is precisely when a person should step in.

That triage is where the agent stops being a mailer and starts being a reputation manager. It watches the responses, sorts the good from the bad, prepares the thank-yous, and flags the problems for you before they become public. Everything outbound still passes your glance, and everything sensitive still lands on a human. The agent handles the volume and the sorting; you handle the moments that matter.