CUSTOMER SUPPORT

Front Edit-Divergence Coaching Log: Track How Agents Rewrite Suggestions

When an agent sends a reply built from a suggested macro, this measures how far the sent text diverged from the suggestion, logs the edit and reason to Airtable.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOutbound reply sent in FrontFront
  • ActionRead sent text and suggested draftFront
  • ActionScore divergence and classify edit reasonOpenAI
  • ActionAppend record to Airtable coaching logAirtableAirtable
  • LogicIf agent crosses rolling drift threshold, branch to coaching
  • OutputSend private coaching DM in SlackSlack

What it does

It quietly tracks how much agents change auto-suggested drafts before sending, building a coaching dataset. For each reply it scores the edit distance, classifies why the agent changed it (tone, accuracy, missing info, or personalization), logs the record, and sends gentle individual feedback when a pattern of unhelpful drift appears.

When to use it

Use it to improve both your macros and your agents. High-divergence macros signal templates worth rewriting; recurring per-agent drift signals a coaching opportunity. This is about quality, not policy enforcement.

How it works

  1. 1An outbound reply sent in Front triggers the run.
  2. 2The sent text and the suggested draft are read from Front.
  3. 3OpenAI computes how far the reply diverged and classifies the edit reason.
  4. 4The conversation, macro, divergence score, and reason are appended to an Airtable coaching log.
  5. 5If an agent crosses a rolling drift threshold, a private Slack DM with a coaching note is sent.
  6. 6Otherwise the run ends after logging.

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
    Connect AirtableBases, tables, views, automations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.