Quick Answer
- The most immediately useful AI agent is often a meeting prep agent that researches people and context before calls.
- It works because it saves attention at the exact moment preparation usually gets skipped.
- The agent needs clear sources, timing, and output rules to be genuinely useful.
I've done a lot of AI demos. Workshops, one-on-one sessions, client calls. And I've noticed a pattern.
When I show email automation, people nod. Calendar management, they're interested. Research tools, they're following along.
Then I show the meeting prep agent.
Something shifts in the room.
People start leaning forward. They start asking questions. And by the end of the demo, everybody wants to know: “Okay, how do I get this?”
It's been true in every single workshop I've run.
So let me tell you what it does, why it works, and how to think about building something like it for yourself.
What the Meeting Prep Agent Actually Does
Here's the core workflow.
An hour before each of your meetings, the agent wakes up. It looks at your calendar, grabs the attendees, and starts researching. It checks LinkedIn. It checks their social media. It scans your email history with that person.
Then it writes you a two-page brief: who they are, what you've discussed before, any relevant context, and suggested talking points.
You read it in 2-3 minutes. You walk into the meeting knowing exactly what you need to know.
Total setup time? Maybe 30 seconds of actual prep on your end. Because the agent already did it.
Compare that to what most people actually do: open a new tab, Google the person's name, scroll their LinkedIn, try to remember what you talked about last time, maybe dig through email for the thread. That's 10-15 minutes per meeting, every time.
If you have 6 or 7 meetings in a day, you're spending close to two hours just on pre-meeting admin. And the thing is, it doesn't feel like a problem. It feels like normal. You've always done it this way.
Why This One Lands When Other Automations Don't
I have a concept I use when helping clients build agents. I call it the 80-20 rule for automation.
The idea is simple: automate the things you do every day or every week first. Not the impressive rare stuff. Not the complex one-off project. The boring repetitive daily loop.
That's where compounding ROI lives.
Meeting prep is daily. For most knowledge workers, it happens multiple times a day. The time isn't all in one place, so it doesn't feel like a problem. But add it up… and it becomes one of the biggest drains on your day.
That's exactly why this agent creates such an immediate reaction when people see it. They calculate it in real time. “I have 8 meetings tomorrow. That's what, an hour and a half I just got back?”
Yeah. Pretty much.
A Real Example: 20 Hours a Week, Down to 15 Minutes
I was working with a client who runs a membership club in Austin. Packed schedule. He was spending something like 20 hours a week on meeting prep and follow-up combined.
We built him a daily briefing system. Each morning, it pulls from his calendar, looks up everyone he's meeting that day, and combines that with the email history and context from his CRM.
He reads it over coffee. 15 minutes. For the whole day.
That's not a small improvement. That's his entire week of prep, automated.
The interesting part: after he saw it working, he wanted to roll out the same system for everyone on his team. Because at that point the ROI is obvious. It's not “AI feels exciting.” It's “this thing just saved me two hours this morning.”
The Tool Selection Question
Here's something I get asked a lot after demos: “Do I build this in ChatGPT or in Lindy?”
The answer depends on what you need it to do.
If you want the agent to actually connect to your calendar, scan your email inbox, and pull LinkedIn data… you need Lindy (or a similar workflow automation tool). That's because you need to connect to external systems. Calendar APIs. Email. CRM.
ChatGPT is great for pure text work. If you want to take a bunch of notes and ask AI to organize them into a brief, ChatGPT is faster to iterate with. But it doesn't natively connect to your Gmail, your calendar, or your contact database.
I think of it this way: use ChatGPT for tasks where the information is already in front of you. Use Lindy when you need the AI to go out and gather the information for you.
Meeting prep requires gathering. So Lindy wins here.
The broader principle is what I call being multi-tool native. The best AI users aren't loyal to one platform. They route work to whichever tool fits the job. ChatGPT for content drafts. Gemini for visual tasks or multi-step research. Lindy for recurring automations that touch external systems.
How to Start Building This
You don't need to build the whole thing at once.
The simplest version: create a Lindy agent that takes a contact's name and runs a Google search plus LinkedIn lookup, then summarizes what it finds.
That's step one. A manual research helper you invoke before each meeting.
Once that's working, you add the calendar trigger. Now it runs automatically an hour before every meeting.
Then you add the email scan. Now it's pulling your actual conversation history.
Each step builds on the last. And each step alone is already useful.
That's the pattern I've seen work with every client. Don't design the full system first. Build the smallest useful version, confirm it actually saves time, then expand.
Most people over plan the agent they want. They want the full Digital Chief of Staff on day one.
Start with meeting prep. That's the thing everyone needs, everyone does manually, and takes maybe an afternoon to get working.
After that, adding features becomes incremental. You're not starting over. You're just adding to something that already works.
Want to see how this works in a live environment? I run one-day AI workshops in Austin where we build agents like this from scratch. Get in touch if you're interested.
