An AI agent for unanswered support-question reports

What an AI agent for unanswered support-question reports should hand you

Before you connect anything, decide what a good weekly report looks like—otherwise you will get a wall of transcripts. The useful version is short and scannable, and every row traces back to a real message. Aim for something like this:

• The question, in the customer’s words—one representative quote, not a paraphrase, so you recognize it instantly.
• How often it came up this week—a simple count across all channels combined.
• Where it appeared—which channels, so you know if it is one loud place or everywhere.
• What happened to it—answered well, answered inconsistently, or went silent with no reply at all.
• The likely gap—a missing doc, an unclear pricing line, a confusing step—and a draft suggestion for the fix.

Why it matters: a report built from those five fields is something you can read over coffee and act on the same morning. How to judge it: pick any row and click through to the original messages—if you cannot, the row is a summary you cannot trust. The realistic failure is scope creep: every extra column feels helpful until the report takes longer to read than the inbox did. That is the trade-off—more detail per question buys nothing once you stop reading to the bottom. Start with these five and add a field only when you have missed it two weeks running. If this is your first agent job, choosing a bounded first task the same way keeps the pilot honest.

Point it at named channels, and keep it read-only

Give the agent a fixed, named list of the places customers actually ask questions—your support inbox, one or two DM channels, the community thread—and grant it read access only. It reads; it never posts, replies, or reacts. Schedule the read to run once a week so the report arrives on a predictable day, and let it land wherever you already look, including the pre-configured Telegram gateway.

Why it matters: reading changes nothing outside the dashboard, so a read-only pilot is close to risk-free—the worst case is a report you ignore. That is the whole logic behind the read, draft, approve, act pattern, and this task deliberately stops at the first step. How to judge it: the agent should be able to name every message it read, and you should never once see it appear inside a thread. The realistic failure comes in two shapes—you point it at “everything,” so newsletters and receipts drown the real questions, or a misconfigured scope lets it draft into the live conversation. The trade-off is coverage: a named list will miss the odd question in a channel you did not include. That is the right trade for a first month—clear signal from a few places beats noisy guesses from all of them.

Rank by frequency, and flag what went unanswered

The agent’s real work is grouping. It should cluster near-duplicate questions—“can I get a refund,” “what’s your refund window,” “how long do I have to cancel” are one topic—count each cluster, and sort the list so the most-asked question sits at the top. Alongside the count, it tags each cluster: answered consistently, answered three different ways, or never answered at all. Grouping messy human text into themes and reading the strong signals off the frequencies is a long-standing research practice; Nielsen Norman Group’s write-up on thematic analysis describes the same coding-and-clustering method the agent is imitating.

Why it matters: frequency tells you where your time buys the most relief, and the “unanswered” and “answered three ways” tags point straight at the leaks—those are the questions quietly costing you customers. How to judge it: open the top cluster and confirm the grouped messages really are the same question; if two genuinely different questions got merged, the ranking is lying to you. The realistic failure is over-merging—a giant “pricing” bucket that hides three separate confusions—or counting your own outbound replies as if they were customer questions. The trade-off is granularity: tighter clusters mean more rows to read, looser clusters mean fewer but blurrier ones. Tune toward tight enough that each row implies one obvious fix.

Read the report as a to-do list for your content

A weekly report is only useful if it changes something. For each question at the top of the list, decide the fix at the source—a line on the pricing page, a clearer onboarding step, one sentence in the docs—rather than a better canned reply. The Government Digital Service put this bluntly years ago in FAQs: why we don’t have them: if a question is frequently asked, it means the answer is missing from where people look. A recurring support question is a content gap wearing a costume.

Why it matters: a canned reply saves you thirty seconds and leaves the question alive; a page edit removes the question. How to judge it: the fix worked if next week’s report shows that cluster shrinking or dropping off the list entirely—the report grades your own edits. The realistic failure is writing a lovely reply, sending it, and never touching the page, so the same question returns on schedule. The trade-off is speed: fixing content is slower this morning than pasting an answer, and pays back over the following weeks. Because the agent keeps persistent memory of what you already fixed, it can tell you when a gap you thought was closed quietly reopens.

Keep every reply human—draft only

The agent drafts suggested answers and doc edits into the report. You are the one who sends the reply and publishes the change. Nothing goes out on autopilot—not even the “easy” questions. This is the boundary that keeps the whole job safe, and it is worth stating in the agent’s instructions rather than assuming it.

Why it matters: a support reply reaches a real person under your name, and a published answer is public—both leave your control the moment they happen, and both are far harder to take back than a draft. That is exactly the kind of action that belongs behind a person, as the field note on where human approval belongs lays out. How to judge it: the agent holds zero send or publish permissions, and you can point to every outbound message as one you approved. The realistic failure is flipping on auto-reply for a category that looked routine—refunds, say—and watching it answer a nuanced case wrongly, confidently, in your voice. The trade-off is real: draft-only is slower than full automation. But a wrong support answer costs trust you cannot refund, and reading channels while you were offline is already most of the value.