HR & RECRUITING

Employee asks about a role; agent drafts the manager intro

When an employee expresses interest in an internal role, an agent reviews their fit and drafts a warm introduction to the hiring manager for HR to review and send.

CategoryHR & Recruiting
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEmployee expresses interest in a role via chat
  • ActionFetch role requirements and employee profileGoogle BigQueryBigQuery
  • LogicAgent reasons over fit and drafts the intro
  • ActionSave draft intro to a Notion review pageNotionNotion
  • OutputAsk HR in Slack to review and approveSlack

What it does

An employee says they're interested in a posted role, and the workflow spins up an agent that reads the role, the employee's skills profile and history, and writes a tailored introduction email connecting the two — highlighting relevant strengths and honestly flagging gaps. HR reviews the draft before anything goes out.

When to use it

Use it to lower the friction of internal applications. Instead of an employee cold-pinging a manager, the agent produces a credible, specific intro that respects both parties' time. Best when fit reasoning needs nuance a fixed template can't capture.

How it works

  1. 1An employee submits interest in a role via a chat message to the assistant.
  2. 2The agent fetches the role's requirements and the employee's skills and tenure from BigQuery.
  3. 3The agent reasons over fit, drafting an introduction that names matched skills, relevant past work, and any development areas, plus a suggested next step.
  4. 4It writes the draft to a Notion review page tagged for the HR partner.
  5. 5It pings HR in Slack to review and approve before the intro is sent to the hiring manager.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect NotionPages, databases, comments.
  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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