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TICKET MANAGEMENT

CEO-agent auto-merge of high-confidence Front duplicates

An agent reviews newly flagged duplicate pairs, decides whether they are truly the same issue.

CategoryTicket Management
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront conversation tagged duplicate-candidateFront
  • ActionPull full history of both conversationsFront
  • LogicAgent classifies pair: confident / borderline / noOpenAI
  • ActionMerge confident duplicate in Front + tagFront
  • OutputEscalate borderline pair to human queue with reasoningFront

What it does

This agent-driven workflow takes candidate duplicate pairs, reads the full conversation context for each, judges whether they should be merged, and acts: confident matches are merged in Front and tagged, while borderline cases are escalated to a human queue with the agent's reasoning attached.

When to use it

Reach for this once your duplicate detection is trustworthy and you want to reclaim agent time by safely auto-closing obvious repeats, without risking wrong merges on ambiguous tickets.

How it works

  1. 1A Front webhook fires when a conversation is tagged as a duplicate candidate by an upstream workflow.
  2. 2The agent pulls the full message history of both the candidate and its suggested parent from Front.
  3. 3The agent evaluates intent, customer, and subject overlap to classify the pair as confident-duplicate, borderline, or not-a-duplicate.
  4. 4A logic branch routes on the verdict.
  5. 5For confident duplicates, the workflow merges the conversation into the parent in Front and applies a "merged" tag.
  6. 6For borderline cases, it posts the agent's reasoning as a comment and assigns the conversation to a human review queue.

Set it up

What you configure once, before turning it on.

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

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