AI AGENTS

Front Out-of-Band Discount Escalation Router

Detects when a prospect's ask exceeds your approved discount ceiling and routes a structured approval request to deal desk in Slack instead of drafting a reply.

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerInbound reply on Front negotiation threadFront
  • LogicExtract requested discount from message
  • ActionFetch list price and approved ceilingPostgreSQLPostgres
  • LogicBranch: ask over the approved band?
  • ActionLog over-band request for auditPostgreSQLPostgres
  • OutputPost approval request to deal-desk SlackSlack

What it does

This agent reads incoming Front negotiation replies, calculates the requested discount against the account's approved band in Postgres, and when the ask is over the line it does not draft a concession. Instead it assembles a one-click approval request for deal desk with the deal context and the exact overage.

When to use it

Use it when reps need a fast, auditable path for asks that exceed standing guardrails. It prevents rogue discounting by forcing anything out-of-band through a named approver, while keeping everything anchored to the original Front thread.

How it works

  1. 1An inbound Front message on a negotiation conversation triggers the run.
  2. 2The agent extracts the requested price or discount from the message.
  3. 3It pulls the account's list price and maximum approved discount from Postgres.
  4. 4A branch compares the ask to the ceiling: in-band asks are skipped, over-band asks continue.
  5. 5For over-band asks it logs the request and overage to Postgres for audit.
  6. 6It posts a structured approval card to the deal-desk Slack channel with approve/deny context and a link back to the Front thread.

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.

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