TICKET MANAGEMENT
Reopened-Ticket Root-Cause Classifier
Detects Zendesk tickets reopened within N days of solving, classifies each reopen as an incomplete fix or an unclear resolution.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionFetch tickets reopened within N days from ZendeskZendesk
- ActionClassify each reopen: incomplete fix vs unclear resolutionOpenAI
- LogicGroup results by class and agent
- ActionTag tickets with classification in ZendeskZendesk
- OutputPost classified digest to support lead in SlackSlack
What it does
It scans recently reopened Zendesk tickets and decides *why* each one bounced back: the original fix didn't hold (incomplete fix) or the customer simply didn't understand the answer (unclear resolution). It tags each ticket accordingly and delivers a daily digest so the support lead sees the split at a glance.
When to use it
Use it when your reopen rate is climbing and you can't tell whether agents are mis-solving tickets or just writing confusing closing replies. The fix-vs-resolution distinction points coaching at the real problem.
How it works
- 1A daily schedule fires the run.
- 2Pull every ticket from Zendesk that moved from solved back to open within the last N days, including the solving comment and the reopening comment.
- 3An OpenAI step reads both comments and classifies the reopen as `incomplete-fix` or `unclear-resolution` with a one-line rationale.
- 4A logic step groups results by class and agent.
- 5Apply the matching tag back onto each Zendesk ticket.
- 6Post a digest to Slack showing counts per class, top offending agents, and example ticket links.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Ticket Management workflows
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Front Duplicate Conversation Clusterer
When a new Front conversation arrives, it semantically compares the report against open conversations.
Intercom Known-Issue Auto-Responder
When a new Intercom conversation matches a known active incident, it attaches the conversation to that incident's parent ticket and sends the customer the current status reply.
Weekly reopen-by-agent coaching digest
Aggregates each agent's solved-then-reopened tickets for the week, identifies the most common reopen reason per agent, and emails a private coaching digest to the support manager.
Escalate repeat reopens to a Linear bug
Detects when the same underlying issue reopens across multiple tickets, uses an AI agent to cluster them by root cause.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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