Advanced

AI Meeting Prep Agent

Before every external meeting, an agent assembles CRM history, recent emails, LinkedIn, and news into a 30-second briefing.

Setup difficulty: advanced

The Problem

Account managers and salespeople walk into half their meetings under-prepared because the prep work — reading the last 5 emails, checking the CRM notes, glancing at LinkedIn for a job change — takes 10-15 minutes per meeting. Multiplied across the day, it's either skipped or it eats an hour. A meeting prep agent runs automatically the night before (or 30 minutes before) and drops a one-page briefing in Slack or email.

Best For

Marketing agenciesConsulting firmsSales teamsAccount management teamsRecruiters

Workflow Steps

1

Trigger on calendar events

Subscribe to the rep's Google Calendar webhook. For each event with an external attendee, trigger the prep workflow N hours before start time.

2

Identify the external contact + company

Parse attendee email → extract domain → look up the company + the specific contact in your CRM. Bail gracefully if not found (still prep with public data).

3

Pull all signals

(a) Last 5 emails between the rep and the contact (Gmail API). (b) CRM activity log: deal stage, last note, open tasks. (c) LinkedIn profile + recent posts (via Phantombuster or manual cache). (d) Company news search (Perplexity API or Tavily).

4

Synthesize into a one-page brief

Prompt the model: 'Write a 6-bullet briefing: who they are, current deal context, last touchpoint, recent signal worth referencing, one question to open with, one risk to watch for.'

5

Deliver via the rep's preferred channel

Slack DM with the brief + a link to the calendar event. Optional: email digest at 7 AM with all of today's meetings stacked.

6

Capture post-meeting feedback

After the meeting, send a one-tap Slack prompt: 'Was this brief useful?' (👍 / 👎). Use signal to tune which signals to include and which to drop.

Copy-Paste Templates

Use these templates as-is or customize for your business.

Briefing Synthesis Prompt
You are creating a 6-bullet pre-meeting briefing for a salesperson. Inputs: CRM data {{crm_json}}, recent emails {{email_thread}}, LinkedIn profile {{linkedin_json}}, company news {{news_results}}. Output exactly these 6 bullets:
• Who they are (1 line)
• Current deal/relationship context
• Last touchpoint and what was promised
• One recent signal worth referencing in the meeting
• One question to open with
• One risk or concern to watch for
Keep it factual. Cite sources inline like [email 2024-04-12] or [LinkedIn] so the rep can verify.
Slack Delivery Format
📋 *Meeting in 30 min: {{meeting_title}}*
👥 With: {{contact_name}} ({{contact_title}}, {{company}})

{{briefing_bullets}}

📅 [Open calendar event]({{cal_link}})  •  💬 [CRM record]({{crm_link}})
Feedback Loop Prompt
After meeting end (poll Calendar 30 min after start time), DM the rep: 'Was today's briefing for {{contact_name}} useful? 👍 / 👎 / Mostly'. Log responses to tune which signals get weight in next iterations.

Orchestration pattern

Multiple specialized agents collaborate: a router/orchestrator delegates to sub-agents (researcher, writer, classifier). Higher capability, more failure surface — invest in observability before scaling.

Learn the agentic glossary →

Failure modes & mitigations

Where this workflow tends to break in production — and what to put in place before you ship it.

Stale CRM data produces wrong context

Mitigation: Surface 'last updated' timestamps in the brief; flag if > 30 days.

Hallucinated quotes from emails

Mitigation: Force the model to quote verbatim with [email date] citations; reject paraphrases.

When NOT to Use This

Skip if your team meets primarily with the same 5-10 contacts repeatedly (briefings become noise). Skip if data sources aren't connected — a partial brief is worse than no brief.

30-60-90 Day Implementation Plan

A phased approach to get this workflow running and delivering ROI.

Days 1–30

Foundation

  • Set up core tools and integrations
  • Configure basic workflow automation
  • Test with a small set of real scenarios
  • Train team on new process

Days 31–60

Optimization

  • Review initial results and adjust triggers
  • Add edge case handling
  • Connect additional data sources
  • Measure time saved vs. manual process

Days 61–90

Scale

  • Roll out to full team or all locations
  • Set up monitoring and alerts
  • Document SOPs for the automated workflow
  • Identify next workflow to automate

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