CUSTOMER SUPPORT

Intercom repro completeness check with escalating follow-up loop

Scores inbound Intercom bug conversations for reproduction completeness and runs a timed follow-up loop.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Intercom bug conversationIntercomIntercom
  • ActionScore repro completeness, list gaps (LLM)OpenAI
  • LogicBranch: complete enough to hand off?
  • ActionAsk for missing fields, tag awaiting-reproIntercomIntercom
  • LogicScheduled re-check: reminder, then close stale
  • OutputClose or hand off conversationIntercomIntercom

What it does

When a customer reports a bug in Intercom, the workflow scores the conversation for repro completeness and, if it falls short, sends a friendly message asking for the exact gaps. A scheduled checker then nudges again after a wait window and finally closes stale, never-completed reports so they don't clog the inbox.

When to use it

Use it for chat-first support where bug reports arrive as casual messages that rarely include steps or environment. It chases the details politely instead of leaving half-formed reports open forever.

How it works

  1. 1A new Intercom conversation classified as a bug triggers the run.
  2. 2An LLM scores the message against the repro rubric and lists missing fields.
  3. 3A branch decides whether the report is complete enough to hand off.
  4. 4If incomplete, Intercom sends a templated ask for the named missing fields and tags the conversation `awaiting-repro`.
  5. 5A scheduled re-check looks at conversations still tagged `awaiting-repro`: it sends one reminder, and on the second pass with no reply it closes the conversation with a courteous note.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  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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