TICKET MANAGEMENT
Agent-Built SLA Recovery Plan for At-Risk Tickets
An AI agent investigates each forecasted at-risk ticket, drafts a concrete recovery action (reply template, internal escalation, or reassignment), applies it in Zendesk.
How it runs
The automated pipeline, trigger to output.
- TriggerPre-shift schedule fires
- ActionFetch at-risk tickets with contextZendesk
- ActionAgent decides recovery action per ticketOpenAI
- LogicBranch on action type
- ActionApply reply, note, or reassignmentZendesk
- OutputPost recovery summary to SlackSlack
What it does
Goes beyond flagging risk to actually working it down. For every ticket the forecast marks as likely to breach this shift, an agent reads the ticket history, decides the fastest path to resolution or a justified deadline extension, and takes the action — drafting a customer reply, posting an internal escalation note, or reassigning — then summarizes what it did.
When to use it
Use it when your team is understaffed against the at-risk volume and you want an agent to clear the easy recoveries autonomously so humans only handle the genuinely hard tickets.
How it works
- 1A pre-shift schedule fires.
- 2Pull tickets projected to breach this shift from Zendesk with full conversation context.
- 3The agent investigates each ticket and decides a recovery action per case.
- 4Branch on action type — draft reply, internal escalation note, or reassignment.
- 5Apply the chosen action in Zendesk (add the draft/comment or reassign the ticket).
- 6Post a per-ticket recovery summary to Slack for lead review.
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.