I run an 8-person startup that uses AI agents. We don't let our agents run on vibes — here's the playbook system we created

• Rachel Woods taught herself AI while studying business in college, later landing a role at Facebook.

• She left to start her own business, which helps people integrate AI into their everyday workflows.

• She said the company has saved itself at least 4 new hires by using AI agents and playbooks.

This as-told-to essay is based on a conversation with Rachel Woods, CEO and cofounder of AMP. The following has been edited for length and clarity.

My first official role in AI was at Facebook. I started as an intern and then joined full-time in 2018. I worked on AI research and development for the ads system that's powered by AI.

I don't have an engineering degree or background; I studied management information systems at The University of Texas at Austin. Part of why I think I got my role at Facebook was because I spent time building my own side projects and teaching myself how to train AI models and do data analysis.

I left Facebook in 2020 to start my own business in the wine industry, and after that company sold, I founded an AI company. I would need to double the number of people on my team if we didn't use AI playbooks.

If you're being thoughtful about setting up your AI operations, it's no different than building a team that you can trust.

That's something a lot of people are missing: how to actually make these systems things you can take your eyes off and not keep yourself up at night in the process.

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I've been an entrepreneur since the age of 15

My first business was a photography business, and I was too young to even drive to meet my clients. I would always catch rides with people, and my parents hated this.

I got into tech because in college, I was exposed to colleagues doing data analytics and working with coding and AI at summer internships. That led me to look online and try to take related classes, and I started to see that if you break a business problem into small enough pieces, you can get AI to handle parts of it: predicting, sorting, generating.

Later, when I was applying to Facebook , I used AI and data analysis to build a report of coffee shops in Austin that were less likely to be too busy for me to study at. I think that project helped me land my role.

After I left Facebook , I started a wine business called Vinebase in 2020; we built a tech platform that used AI to help winemakers sell their wine online and manage the digital side of their business.

Because this was before AI became more mainstream, we didn't market it as AI. We called it our "auto magic" system. I think that if we had said it was AI, it could've been a turnoff for our customers. Vinebase was acquired by Corksy in fall 2022.

I use my playbooks and AI agents to replace additional staff

My sister and I started AMP, originally called The AI Exchange, before ChatGPT was released in November 2022, because I was using AI in Vinebase and saw how the technology was progressing. ChatGPT came out about a month later and started really accelerating the AI timeline, so we went all in.

We have four full-time employees and four part-time contractors right now, but we'd need double if we weren't running as much as we are on AI. Given the company's current size, we'd need at least four to five additional people, including a sales and marketing manager, customer support specialist, executive assistant , and operations coordinator.

The way we structure these systems is based on something we call playbooking. Every department in the company has its own system. Each system is a collection of playbooks with AI agents that can run independently or together.

How we build our AI playbooks

To build an AI playbook, pick one piece of work you do over and over, and write down what starts it, what goes into the task, and what should come out at the end. Next, write the steps the way you'd train a new hire: each step, the decisions in it, and what "good" looks like.

Start running it with AI, and while you watch, compare the result to how you would've done it. Then, fix the playbook, not the output, until you trust it to run without you.

I always think through how I want a playbook to work and outline it by hand. Whenever you're making an AI playbook, you want to own the thinking and the logic. Otherwise, you can fall into the trap of including something that you don't really understand, and then you're not going to be able to trust that system anyway.

We have an AI agent running our sales playbook to review customer calls and ensure they meet our standards, rather than having a sales manager review them manually. Also, instead of sales team members needing to ask their manager questions, they just ask the playbook, which then pulls the answers from our guide for that team.

There are two types of AI costs we have in the business. The first is the subscriptions to Claude and ChatGPT for every person on the team. The other is because the majority of our operations are these AI playbooks that are running in the background without us being the ones starting or triggering it. In September, we spent $8,510 on Claude Code to run our playbooks in the background, which is billed by usage and is separate from the team's monthly Claude and ChatGPT subscription costs.

Own the playbook, rent the tech

We say "own the playbook, rent the tech," which means that if you are creating really good playbooks for your AI to follow, it actually doesn't matter which tool you're running it in.

Right now, we have a setup that's running with Claude Code in the background. But I could run all of these playbooks in ChatGPT, Grok, Gemini, or Microsoft Copilot .

We try to avoid a lot of the risks that people are concerned about with AI going rogue by creating these detailed outlines. We don't just want the agents to run on vibes. There still needs to be a person to decide and design how the work gets done; AI just speeds up everything else. That's the job security we have in this AI era.

Start small, and get everyone involved

A few of us on the team who are more advanced with AI own the bigger, team-wide playbooks that the business depends on, but everyone playbooks their own personal workflow, and then we use that as the testing ground to graduate things into becoming more part of official systems.

My advice would be to just start with something small and really easy, because once you get your first win and see how AI can do that repetitive work for you, it's kind of like a snowball.

Do you have a story to share about using AI to run your small team? Contact this editor, Agnes Applegate, at [email protected] .

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