Reply to client tickets and email daily ticket summaries
Seeing a lot of buzz about AI tools that supposedly auto-generate documentation from tickets, create KB articles automatically, or use LLMs to surface relevant past resolutions. Task: Seeing a lot of buzz about AI tools that supposedly auto-generate documentation from tickets, create KB articles automatically, or use LLMs to surface relevant past resolutions. Curious if anyone here has actually implemented something like this that works. Examples would be tools that watch your ticket workflow and create KB articles automatically, AI writing ticket summaries or resolution notes, anything to eliminate manual documentation. It would be great to know what tools other people have tried, what problem they solved vs what they promised, and if engineers are actually using it or if it is shelfware. Also, where these tools fell short, and what made you stop using them? I'm trying to figure out if this is genuinely solved territory or if we're still in the "sounds good on paper" phase. Our team is sceptical about adding another tool that promises to fix documentation but ends up being more work to maintain than just doing it manually. Would love to hear real experiences - both success stories and cautionary tales.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include access to ticketing system APIs and clear data privacy policies. Constraints involve potential hallucinations in AI summaries and compliance risks with client data. Human approval is mandatory for all published content to mitigate shelfware risks..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
Seeing a lot of buzz about AI tools that supposedly auto-generate documentation from tickets, create KB articles automatically, or use LLMs to surface relevant past resolutions.
Workflow
Seeing a lot of buzz about AI tools that supposedly auto-generate documentation from tickets, create KB articles automatically, or use LLMs to surface relevant past resolutions. Curious if anyone here has actually implemented something like this that works. Examples would be tools that watch your ticket workflow and create KB articles automatically, AI writing ticket summaries or resolution notes, anything to eliminate manual documentation. It would be great to know what tools other people have tried, what problem they solved vs what they promised, and if engineers are actually using it or if it is shelfware. Also, where these tools fell short, and what made you stop using them? I'm trying to figure out if this is genuinely solved territory or if we're still in the "sounds good on paper" phase. Our team is sceptical about adding another tool that promises to fix documentation but ends up being more work to maintain than just doing it manually. Would love to hear real experiences - both success stories and cautionary tales.
Reported result
No measured results are available as the source is a request for experiences rather than a case study. Proposed acceptance check: Track the reduction in manual documentation time per ticket and monitor the error rate in AI-generated KB articles over a 30-day pilot period.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include access to ticketing system APIs and clear data privacy policies. Constraints involve potential hallucinations in AI summaries and compliance risks with client data. Human approval is mandatory for all published content to mitigate shelfware risks.
Try it
Act as an IT documentation specialist. I need to pilot an AI workflow for our MSP ticketing system. [Replaceable inputs: Ticketing system name, API access details, KB platform, sample ticket IDs]. Steps: 1. Connect to the ticketing system and retrieve 5 closed tickets with resolutions. 2. Use an LLM to generate a concise summary and a draft KB article for each. 3. Present the drafts to me for review. 4. Do not publish or send anything until I explicitly approve each item. Output format: A table with columns: Ticket ID, AI Summary, Draft KB Article, Confidence Score. Quality measure: Success is defined by my approval of at least 3 out of 5 drafts without major edits. Error handling: If API access fails, ask for CSV export of tickets. If the LLM output is too generic, ask for specific technical details from the ticket notes. First, perform a dry run on one sample ticket and show me the result before proceeding to the full batch.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.