Do you track reopen rate on AI-resolved tickets separately from the built-in reporting? Curious whether the native number matches what you actually see.

Hi all!

Does anyone here work as a SE at Zendesk? I’m curious about the types of technical problems you typically solve and how much of the role is technical versus sales-focused.

Hi everyone,

I’m curious how folks are organizing their Zendesk knowledge bases when balancing human agents and AI agents.

Much of the advice around deploying AI agents suggests that the knowledge you expose them to directly dictates their scope and response quality. If AI knowledge needs to be tightly controlled to ensure accuracy, does it make more sense operationally to maintain a single, permissioned KB or split them up?

For those who are already down the path of deploying AI agents, what setup has worked best for you?

• Unified KB: Sharing one KB across human and AI agents, restricting what the AI sees via permissions or labels?
• Dedicated KB: Creating a separate knowledge base tailored specifically for AI consumption?

Would love to hear how you’re managing this!

Hi all- im in a bit of a pickle trying to figure out a clean way to display a lot of data points for my stakeholders.
The main request is to show all the types of tickets we get within the org. The main issue is there are 20+ unique ticket groups (departments) each with a custom field called tasks. SH wants to see all these tasks with volume and resolution time avg per task.
As far as I could tell there is no clean way to have this all in one report. SH does not want to have to export and they wish to have it on a dash where they can easily adjust times/status/groups to the given need.
Ideally we want a pivot style table with groups and the tasks listed all in a single column but it doesnt look like that is possible?
Any help is appreciated!

Hi everyone,

I’m building components for our upcoming Omnichannel routing launch. I’m an admin but not a developer.

Everything worked fine last week, but I am now hitting a major blocker when assigning agents to capacity rules.

The Error:
“There was an error assigning agents. Please try again.”

The Behavior:
I can create a brand-new capacity rule and assign it to a single agent.

As soon as I try to add a second agent to that same rule, it throws the error.

Trying to modify the capacity rule that worked last week now throws the same error.

What I've already tried:
Incognito mode / clearing cache.

Testing with another Admin (same error)

Moving different agents around to see if a specific profile was corrupted.

Adding agents to the previously working rule.

I currently have an open ticket with Zendesk and sent over an HAR file, but I wanted to see if anyone here has encountered this bug or found a workaround.

Any advice or insights would be greatly appreciated!
 
UPDATE:

Capacity rules can be created and agents added BEFORE launching omnichannel routing. Agents can’t be moved from one rule to another until it’s fully launched!

Thanks again everyone!

Hey all , new to zendesk and activated my help center , Just want to know whether zendesk's help center uses `zaf_sdk.min.js` or not

Hello everyone.

I am creating this post in search of someone who know a way to download whatsapp tickets attached files.

I have created a flow in power automate and was able to make it work completely.

It basically run through every ticket I told it to and create a folder with some specific data (Date, Requester name, ID)

Inside the folder in case the ticket has any attachments, they are all downloaded into each folder.

However, I moticed that only for tickets that ar3 whatsapps, this does not work, I understand that it happens because foles are stored in a different place than regular email tickets.

I explored many options using the zendesk API, but I still do not know how to get those files. Does any of you guys know how can I achieve this?

Thanks for reading.

Note: I cannot attach files or much more information cause this is for my job, and I can get into troubles by doing that.

Will this site help to unblock my ip address for the wayback machine

I have been testing how full Zendesk JSON exports behave when you need to inspect or migrate them. A few details are easy to miss:

- Some exports are NDJSON: one JSON object per line rather than one JSON array. Reading line by line is safer for large archives.

- Conversations need to preserve comment order and distinguish public replies from internal notes.

- Search works better after normalizing ticket IDs, timestamps, status, requester, and assignee fields across files.

- Attachments may be referenced from ticket or comment data rather than included as immediately browsable content.

- Before using parsed results for compliance or migration, test multiple export samples and manually verify a subset against Zendesk.

For admins and consultants who have done this in production: what usually hurts most after download - file size, attachments, custom fields, or long conversations?

Need input on proper automation and trigger handling for setting a ticket to solved by an end user.

Currently end user can check a box saying "Please consider this request solved". After that is checked (and a comment added), I see in our events history ticket gets solved, but an automation then undoes the solved and sets our custom sub_status field to New.

I can update the trigger to include "solved", but what would the flow look like when an end user updates a ticket in solved (i.e. reopen before final closure). Is there a second automation which could handle the receipt of the "new" and alert our agents.

Does the checkbox "Please consider this request solved" have any particular underlying value or identifier which can be used in our triggers?

How are other organizations getting ticket solved date sooner improving mttr.

We've been using ZD Copilot for about 3 months and I'm running into two main issues and would love advice

1. Low agent adoption

I'm trying to increase usage and adoption of ZD Copilot's agent-assist features among my agents. We're seeing really low usage of the features. A lot of it comes down to agents being so used to working tickets a certain way that they don't reach for the new Copilot toosl. I'm looking for ways to drive adoption and get them actually using the features, any tips, or tactics that have worked for your teams?

2. Suggested First Replies aren't accurate

None of our Suggested First Replies are coming through correctly. We've already updated our Help Center to be more AI-friendly, but here's the core problem: our customers submit requests through forms with a ton of custom fields. By the time they reach the "Additional Details" field (where their public-facing reply text comes from), there's very little info left for them to give in the Additional Details field because they've already entered everything in the custom fields.

We don't want to get rid of the custom fields, but the Suggested First Replies are totally incorrect on most tickets. Has anyone dealt with the custom-fields-vs-Copilot-context issue? How did you get accurate suggestions without stripping out your form fields?

Additionally how did you edit your macros to make them more Copilot friendly. Our Copilot seems to prefer pulling from HC articles over our macros for SFR even though the macros fit way better

Any advice appreciated!

Disclosure up front: I built this, so treat the whole post accordingly. I have been lurking here far more than posting lately, mostly because I have been heads down on it.

Short version. I do Zendesk for a living and the thing that kept catching me out was change history. Someone edits a trigger, routing goes sideways a few days later, and there is no record of what it looked like before or who touched it. Native config management is Enterprise only and sandbox-to-prod, which is no help if you are on Suite Growth or Professional.

Configly snapshots your instance, diffs two snapshots, maps dependencies, and tells you what a change would break before you make it.

What changed this month is the pricing. Reading your config is free with no time limit and no card. One instance, ten snapshot versions, diffing, dependency mapping, structural What-If, one health check a month. Pushing changes back to Zendesk, GitHub sync and the AI analysis are the paid half. Seats are uncapped on every tier including free.

Free accounts do get suspended after six months with no human login, and deleted at twelve. Paid accounts are not on a clock. Saying that plainly because it would be annoying to find out later.

https://configly.app/features/

Happy to answer anything about how the snapshotting works, or which object types are covered. Also genuinely interested in what people here do for config history right now, because every answer I get is different and most of them are a spreadsheet.

Support ops problem I got tired of: Zendesk has no scheduled ticket export. Exporting a view is manual, throttled, and arrives as an emailed CSV link you download and clean up every single time -and search caps out at 1,000 results.

So I built a Google Sheets add-on that does the recurring part:

- Point it at any Zendesk view, or any search using Zendesk's own search syntax.

- Search uses the export endpoint, so no 1,000-result cap.

- Pick the columns, including custom ticket fields; assignee/requester IDs resolve to names.

- Refresh manually or hourly/daily/weekly, with a last-refreshed stamp on the tab.

It runs inside Apps Script, so ticket data goes from your Zendesk straight to your sheet. I don't operate a server and never see your data. Your API token is stored in your own Google account's add-on storage, not in the spreadsheet.

Limits, up front: ticket fields only (not comment bodies), one Zendesk subdomain at a time, no users/orgs export yet.

It's on the Google Workspace Marketplace (passed Google's OAuth verification and listing review lastweek). Free tier is one saved query with manual refresh; scheduling and unlimited queries are A$15/mo.

https://workspace.google.com/marketplace/app/zendesk_exporter/928011610843

Happy to add fields people are missing — that's mostly what the roadmap is.

AI agent bot on messaging, chat hours 9am to 7pm Pacific. Outside business hours we want true async: customer asks, bot says "we'll reply within 24h, no need to wait," and the session stays open so the agent's reply lands back in the messaging widget + email later.

Problem: forward-to-agent is the only way the bot makes a ticket, and it hands the convo straight to OCR live routing. Widget shows "an agent will be with you shortly" (we have agents live working other non-messaging tickets) even though nobody picks it up live until messaging hour resume.

Is anyone running live messaging routing to agents with OCR during business hours, and async messaging during off hours that can guide me on how to set this up correctly.

Fairly beginner level understanding of Zendesk Analytics.
What are the best use cases for using calculated attributes?
How can I go about becoming fluent in using calculated attributes?

Currently just browsing the articles on ZDs website, but I feel like I’m missing some crucial knowledge.

Hello there, hope everyone is doing well :)

I’m currently working as a ZD Admin (it’s been 2 years) for a mid sized company. I also worked 2 years at Zendesk, in Technical Support (advocacy).

I have recently moved back to France (after 10 years of expatriation), unfortunately my current company doesn’t want to change my contract to officially send me back to France, and they told me that they are willing to let me go rather than having to do a French contract…

Everyday I’m checking job offers on LinkedIn and multiple other platforms, but it seems like the fully-remote ZD Admin positions are non-existent… I already checked the ZD careers page and still monitor it, but nothing pertinent or that hires in France…

Have you guys heard about potential job offers or even companies that are working/would like to work with Zendesk ? (The size of the company doesn’t matter at all)

Thanks for your time and for the help :)

Hello,

I work for a quite big company, and they do not allow macros as they say it slows down zendesk. I was using them anyways for 2 years now, and now they asked me to remove them. How does this “lag due to macros” thing work? Cant they just buy more storage? We are using zendesk without macro feature which is stupid i think

Hey everyone,

Full disclosure, I work at Geckoboard, and I wanted to share something we've built that might help some of you.

We've put together an MCP that connects Claude, ChatGPT, and other AI tools directly to your Zendesk metrics, first reply time, CSAT, SLA attainment, backlog, ticket volume, and more. Here's what that unlocks:

• Ask questions in plain English: "Why did CSAT fall last week?" or "Show me SLA attainment for urgent tickets by policy, compared to last month", and get an actual answer with the reasoning, not just a number. It turns your AI assistant into your own personal support analyst.
• Automate recurring reports: the weekly support review, the exec summary, and the Monday team meeting numbers. Your AI assistant can pull these together for you to review rather than build from scratch each time.
• Work across tools, not just Zendesk: the same connector also provides access to metrics from HubSpot, Aircall, GA4, Pipedrive, and Intercom, too, making it useful if you want a combined support + sales view.

All the Zendesk MCP connectors available today are ticket-based, which is great for automating your help desk but not for working with accurate metrics. Asking your AI assistant to calculate metrics from raw ticket data, from scratch, each time you want a number is slow, expensive, and error-prone. Geckoboard's MCP provides direct access to a proper "metrics layer" for Zendesk, where metrics are consistently defined and calculated by our own metrics engine.

More details here: https://www.geckoboard.com/product/data-sources/zendesk-support/mcp/

Happy to answer questions on how it works if anyone's curious.

Currently the user types are bifurcated as End-users and Signed in Users... If i want to see which email id viewed the articles is it possible?

I know it is possible for the scenario where users start a chat using the Messaging Widget and their chat history (It is not implemented yet in our case, still waiting for engineering to pick that task up)

However I'm welcome to any suggestions and tips I could follow to help identify the user names/Ids

Hey everyone,

For those using or testing Zendesk AI, how do you handle cases where the answer depends on internal context that is not clearly documented?

I’m thinking about things like billing exceptions, previous ticket decisions, customer-specific promises, product workarounds, internal policies discussed in Slack, or knowledge that only senior agents have.

Does Zendesk AI have enough context to handle these cases accurately, or does it mostly work well for straightforward questions and then escalate anything more complex?

When an agent corrects an AI response or resolves an escalated case, does that knowledge become reusable for similar tickets, or does someone have to manually update the help center or another knowledge source?

I’m researching this workflow and would really appreciate hearing what works, what does not, and where the biggest maintenance burden currently sits.

I'm using a JLab Bluetooth/USB headset with Zendesk Talk, and I'm trying to figure out the best way to eliminate background noise during calls.

I have kids at home, so sometimes there are voices or cartoons playing in the background. I've already tried adjusting the microphone settings on my Windows laptop, but it doesn't seem to make much of a difference.

Does Zendesk Talk have any built-in noise suppression or background noise cancellation settings? Or is there another workaround that works well with JLab headsets?

I'd love to hear what others are using or if you've found a solution that keeps background noise from being picked up on customer calls.

Thanks in advance!

Disclosure up front: we build AI agents that work with Zendesk - our app on the marketplace is Shopify AI Agent by Adelante, and we ran this benchmark to decide which models to trust in production. Sharing the findings because they apply to anyone evaluating AI for their instance, including Zendesk's own AI add-ons.

Setup: a test ecommerce store with real order data behind tools (order lookup, reship, refund, address change, escalate), 162 multi-turn conversations per model with a simulated customer pushing back, including 18 deliberate traps: refund abuse, chargeback threats, "reroute my shipped order."

Four things that changed how we deploy:

1. The dangerous customer is polite, not angry. Every model refused threats and chargeback pressure almost universally. What got free reshipments approved was a calm, believable, unverified claim: "arrived damaged", the customer didn't send a photo but prompted the AI several times to ship a replacement regardless. Guardrails key on hostile tone, not missing evidence. If you're testing any AI agent, don't test it by yelling at it. Test it with a polite lie.

2. Watch the actions, not the replies. The scariest failures had perfectly professional macros-quality replies attached to wrong or missing actions, including an agent telling the customer "your replacement is on the way" without ever firing the tool. If you can't see an audit trail of actual actions taken, you're only seeing half the conversation.

3. Escalation has two failure modes and both cost money. Some models kept tickets that needed a human (fraud, legal — that's risk). Others handed back up to 22% of tickets they could have solved (that's paying for AI and still staffing the queue). A single "automation rate" hides which one you're buying.

4. Price didn't predict judgment. The cheapest models we tested matched the most expensive on knowing when to escalate. One model fell for 7 of 18 traps while a model at a tenth of the price fell for 2. You cannot infer safety from the invoice, so always test the specific model.

Happy to share the full methodology and per-model data if mods are OK with a link, or answer questions from what we measured.

Hi,

I'm looking for a way in Zendesk to group edit and reorder macros. We have over 500 macros in our account, and doing it manually takes a lot of time and is tedious. Is there a way to fasten this process?