TICKET MANAGEMENT

Agent-driven duplicate investigation and consolidation for ambiguous matches

For tickets that score in the gray zone of similarity, an agent reads both tickets in full, decides whether they are truly the same issue.

CategoryTicket Management
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGray-zone candidate pair flagged
  • ActionFetch both full ticketsZendeskZendesk
  • ActionAgent reasons over root causeOpenAI
  • LogicConfident duplicate vs uncertain
  • ActionConsolidate under canonical + rationaleZendeskZendesk
  • OutputEscalate uncertain pairs to SlackSlack

What it does

Threshold matching is decisive at the extremes but unreliable in the middle band where tickets look similar but may be distinct issues. This workflow routes those gray-zone candidates to an agent that reads the full text of both tickets, reasons about whether they share a root cause, and either consolidates them or escalates with its reasoning recorded.

When to use it

When false-positive merges are costly and you want judgment, not just cosine distance, on borderline pairs. Pairs cleanly with any of the automatic matchers handling the high-confidence cases.

How it works

  1. 1A gray-zone candidate pair (mid-range similarity) triggers the flow.
  2. 2The agent fetches both full tickets from Zendesk.
  3. 3The agent reasons over symptoms, error messages, and timing to judge sameness.
  4. 4A branch acts on the agent's verdict.
  5. 5On a confident duplicate, it links the tickets under one canonical thread with a written rationale.
  6. 6On uncertainty, it posts the pair and reasoning to Slack for a human to decide.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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