AI AGENTS

Front Counter-Proposal Drafter with Discount Guardrails

Watches a Front inbox for inbound pricing pushback and drafts a counter-proposal reply that stays inside your approved discount ceilings.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerInbound message on tagged Front conversationFront
  • LogicClassify: is this a discount/price request?
  • ActionLook up account tier and approved discount ceilingPostgreSQLPostgres
  • ActionDraft on-policy counter-proposal reply in FrontFront
  • OutputNotify deal owner that a draft awaits reviewSlack

What it does

When a prospect replies asking for a lower price, this agent reads the full Front conversation, looks up the account's approved discount band, and writes a ready-to-send counter-proposal that concedes only what policy allows. The draft is attached to the same Front thread so a human can review and hit send.

When to use it

Use it when your reps negotiate over email in Front and you want consistent, on-policy responses instead of ad-hoc discounting. It removes the back-and-forth with deal desk for routine asks while keeping a human in the loop on send.

How it works

  1. 1A new inbound message lands on a tagged Front conversation and fires the trigger.
  2. 2The agent reads the thread history and classifies whether the message is a discount/price request.
  3. 3If it is, it queries Postgres for the account's tier, list price, and maximum approved discount.
  4. 4The agent composes a counter-proposal that anchors value and offers a concession at or below the ceiling.
  5. 5It writes the reply as a draft on the Front conversation for rep review.
  6. 6A Slack note tells the deal owner a draft is waiting.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  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.

Run this workflow in your colony.

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