Recommend next-best-actions for support agents
When a new support ticket arrives, the agent in front of it often has to solve a problem the team already solved dozens of times. NEXT reads your resolved cases and knowledge articles, then matches each new ticket to how similar issues were actually fixed. The agent opens the ticket and sees the likely next step and the relevant article already attached.
Most of that knowledge already exists. It is just buried in closed tickets the current agent never saw.
What the next-best-action recommendation looks like
Example of what an agent sees after NEXT matches a new ticket to resolved cases.
Incoming issue
A customer cannot update the beneficiary on a life policy after a name change. The online form keeps rejecting the new details.
Matched resolved cases
14 closed tickets in the last 90 days show the same rejection, all traced to a mismatch between the legal name on file and the document the customer uploaded.
What customers said
"I changed my name after getting married and the system won't accept my new ID. I've tried three times now."
"The beneficiary form keeps erroring out and there's nothing telling me what I did wrong."
Recommended next step
Check that the legal name on the policy matches the uploaded ID exactly. If it does not, update the legal name first, then resubmit the beneficiary change. The form rejects silently when the two do not match.
Relevant knowledge
Internal article: "Name change before beneficiary update" — the two-step order most agents miss on the first attempt.
Similar tickets open now
9 currently in the queue match the same pattern.
Signal strength
Strong and consistent — one root cause across all 14 resolved cases.
The agent did not search for any of this. It was waiting in the ticket.
How NEXT does this
NEXT reads where your support work already happens: resolved tickets, internal knowledge articles, and the notes agents leave when they close a case. It keeps a continuously updated record of how each kind of issue was fixed and which fix actually held. When a new ticket arrives, NEXT matches it against that record and writes the most likely next step and the supporting article into the ticket the agent is already working in. The match lands where the work happens, not in a separate tool. The agent reads it, decides whether it fits, and resolves the ticket — or overrides it. NEXT brings the prior resolution to the ticket; the agent still chooses the response.
Why agents keep re-solving the same issue
Most support teams have the answer somewhere. The problem is retrieval. The resolution lives in a closed ticket from three weeks ago, in a senior agent's head, or in a knowledge article nobody can find under the search terms they tried. Each handoff strips detail: the original fix gets shortened into a macro, then half-remembered, then re-derived from scratch by the next agent who catches the same issue.
The tools meant to fix this both wait. Open a knowledge base and it shows what someone wrote down — and only if the agent stops mid-ticket to search it. Ask an AI assistant and you get the most generic article on the topic, not the specific fix that resolved this exact issue last month. Neither one comes looking for the agent while the customer is on the line.
A faster knowledge base still waits for the agent to search it.
For a support operations leader who already lost the headcount fight, this is the volume problem in miniature. The same issue gets reopened, escalated, and re-solved because the resolution never traveled to the next agent.
How this compares to the tools you already know
Approach | Where the resolution lives | What the agent does at decision time |
|---|---|---|
Knowledge base search | In articles, if someone wrote and tagged them well | Stops, guesses keywords, scans results |
Macros and canned responses | In pre-written templates | Picks a template that may not match the root cause |
Generic AI assistant | In a model's general training | Asks, gets a plausible answer, then verifies it |
NEXT | In your resolved cases, matched to this ticket | Reads the suggested next step already attached |
What changes for the support operations team
Today, your newest agents escalate issues your senior agents quietly solved months ago. The fix existed; it just never reached the person handling the next ticket. So contact volume holds steady, ramp time stays long, and your most experienced people spend their afternoons answering the same internal questions.
With NEXT, the new agent opens the beneficiary-rejection ticket and the prior resolution is already there: the two-step order, the article, the nine other open tickets with the same root cause. The ticket looked like a quick fix until you saw it had been reopened twice. Now it gets resolved on first contact instead of routed to a queue that adds a day.
You can also see the pattern as it forms. Nine matching tickets open at once is not nine separate problems — it is one form behaving badly, and now you can route it to product with the evidence attached instead of absorbing it as steady-state volume.
The agent still chooses the response. NEXT supplies the resolution that worked before; it does not decide what to send.
Downstream effects
Repeats get caught as repeats. When NEXT shows that nine open tickets share one root cause, you can fix the cause once instead of resolving the symptom nine times — and feed the pattern to the team that owns the form.
Ramp time shortens. A new agent backed by the team's resolved cases handles harder tickets sooner, because the institutional knowledge is attached to the work rather than locked in tenure.
Escalations thin out. Issues that used to bounce to a senior agent for a known answer get resolved at first touch, freeing your experienced people for the genuinely novel cases.
Where the human stays in control
NEXT writes a suggested next step; it never sends a response on its own. You set how strong a match has to be before NEXT surfaces a recommendation, and you can require a human to review matches before they are written into low-confidence categories. Sensitive workflows — claims decisions, policy cancellations, anything with compliance weight — can be held back from recommendations entirely. That is configuration work: you tune the thresholds and the scope, and the agent keeps every resolution decision.
What to get right before you turn it on
The recommendations are only as good as the resolved cases behind them. Start where your ticket history is clean and the close notes describe what actually fixed the issue, not just "resolved." Categories with thin or messy history will produce weak matches, so scope NEXT to your high-volume, well-documented issue types first and expand as coverage improves.
Decide your match threshold deliberately. Set it too loose and agents learn to ignore the suggestions; set it too tight and the system stays quiet on issues it could have helped with. Confirm where the recommendation appears so it sits in the agent's line of sight inside the workspace, not in a tab they have to open. And keep a feedback path: when an agent overrides a suggestion, that signal should sharpen the next match.
Where this breaks down
The resolution history is thin or wrong. If close notes say "fixed" without saying how, NEXT has little to learn from. Categories with sparse or inaccurate history produce weak matches, and weak matches train agents to stop reading them.
A genuinely new issue has no precedent. For a first-of-its-kind ticket, there is no resolved case to match. NEXT will say so rather than force a stretch, and the agent works it cold — which is correct, but it means novel issues see no lift.
The old fix is now out of date. A resolution that worked before a policy or system change can resurface as a recommendation after it has stopped being right. Without a path to retire stale fixes, NEXT can confidently suggest yesterday's answer.
The threshold is mistuned. Too loose floods agents with marginal matches; too tight leaves obvious repeats unaddressed. The quality of recommendations lives or dies on calibration, and that needs a few weeks of override feedback to settle.
FAQ
How is this different from knowledge base search?
A knowledge base waits for the agent to stop, guess search terms, and scan results — and it only contains what someone took time to write down. NEXT works from your actual resolved tickets, matches the current issue to how similar ones were fixed, and writes the suggested step into the ticket without the agent searching. It surfaces the specific fix that held, not the closest-sounding article.
Does NEXT decide how to resolve the ticket?
No. NEXT brings the prior resolution to the ticket and suggests a next step. The agent reads it, judges whether it fits this customer, and chooses the response — or overrides it. Sensitive workflows like claims and cancellations can be scoped out of recommendations entirely, so judgment on those stays fully with your team.
What if the recommended action is wrong?
The agent overrides it, which is the expected control point — recommendations are advisory, not automatic. Overrides are useful signal: they tell NEXT where a match missed or where an old fix has gone stale, which sharpens the next recommendation. You also set the match threshold, so you decide how confident a match must be before it surfaces at all.
Does this work for brand-new issues with no history?
Not directly, and it shouldn't pretend to. If there is no comparable resolved case, NEXT says so rather than forcing a weak match, and the agent works the ticket cold. The value concentrates on your repeat, high-volume issues — which is also where most of your contact volume actually comes from.
How does this reduce support volume?
It attacks repeats two ways. First, agents resolve known issues on first contact instead of escalating or reopening them, which removes the repeat touches a single ticket generates. Second, when NEXT shows many open tickets sharing one root cause, you can fix the cause once and stop the inflow — turning a recurring queue into a single resolved problem.
Where does the recommendation appear?
Inside the agent's existing workspace, attached to the ticket they are already handling — not in a separate dashboard or assistant they have to open and query. The goal is that the prior resolution is in their line of sight as they read the issue, so there is no extra tool to adopt and no search step to remember.