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AI agent for help desk technicians

How-To Ticket Training Gap Agent

Fewer how-to tickets through training aimed at the topics and teams that generate them

How-To Ticket Training Gap Agent: what goes in, what the agent does and what you get

What it does

A large share of tickets are how-to questions that training could prevent. This agent separates how-to tickets from faults, groups them by tool and team, and ranks the topics by volume and cost in technician time. It drafts a short training plan for the top topics, with the audience and format. After the training, it checks whether tickets on those topics fall. If they do not, it looks at why, such as the wrong audience, and changes the plan. The lead approves the training topics and the invitations. Edge case: one team asks the same spreadsheet question each month, so the agent proposes a short team session in place of a company-wide course.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
ApprovedYes, continueNo 1 STARTS WHEN Monthly ticket review 2 USES A TOOL Classify tickets as how-to or fault 3 DOES Group how-to tickets by tool and team 4 DOES Rank topics by volume and technician time 5 DOES Draft a training plan with audience and format 6 YOU APPROVE Lead approves the topics and invitations 7 USES A TOOL After the training, count new tickets on thosetopics 8 CHECKS THE RESULT Did the tickets fall by the target share? If not: look at the audience and format, change the planand propose a new session. Back to step 3. 9 DOES Record the result and update the ranking 10 RESULT Training report and next month's topics
Read the steps as a list
  1. Monthly ticket review
  2. Classify tickets as how-to or fault
  3. Group how-to tickets by tool and team
  4. Rank topics by volume and technician time
  5. Draft a training plan with audience and format
  6. Lead approves the topics and invitationsThe agent waits here for your OK.
  7. After the training, count new tickets on those topics
  8. Did the tickets fall by the target share?If not: look at the audience and format, change the plan and propose a new session. Back to step 3.
  9. Record the result and update the ranking
  10. Training report and next month's topics

How it decides

A topic is chosen when it is frequent and costly. A training counts as working when related tickets drop by a set share in the next period.

  • Rank by volume times technician time
  • Choose a team session when one team has most tickets
  • Count a drop only against the same period length
  • Retire a topic when tickets stay low

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Target drop in tickets (default: 40%)
  • Time window for comparison
  • Training formats allowed
  • Number of topics each month (default: 3)

What keeps you in control

It always asks you first

  • Lead approves topics and invitations

Hard limits

  • Never sends invitations itself
  • Never uses ticket text outside the IT team

It stops when

  • Done: targeted topics fall and the report is saved
  • Stop: ticket data does not separate how-to from fault

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensOf 900 tickets, 210 were how-to, and 64 were about shared calendars in the sales team. The agent proposed a 30 minute session for sales. A month later, tickets on the topic fell from 64 to 19, a 70% drop, which passed the 40% target. Another topic fell only 10%, so the agent changed it to short videos.

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