Your AI Agents Should Go to School Too: Give Every Hire a Learning Path

Should your AI agents keep learning after you hire them? Yes. I give every agent an education path, the same way I'd give a human hire one. My agents can get a daily study routine: read, watch, write a synopsis, then add what they learned to their skills and their journal. And a pair of support bots checks the whole system and brings me fixes as suggestions. I still decide what changes.

Here's the question that started it for me. As I put it in a live training this fall, "if you hire humans, do you not give them an educational path that they have to do this quarter this year? So why would you not give your agents an educational path so they learn over time as well?"

Hiring Is Only Day One

I've already written about how I hire an AI agent. Job description first. Hard skills, soft skills, who it reports to, guardrails.

But a hire isn't finished on the first day. Not for humans, and not for agents.

My personal mindset is a growth mindset. I want my second brain to get better over time. So, in my words, "I built these agents in a way that they would actually get better over time."

When I hire an agent, I still start the same way. "I want you to go do deep research on the hard skills and soft skills that the best designer that I could hire would have." From there, I give it a personality, operating modes, a skills inventory of what it can and should do, and the boundaries of what it needs to ask me about.

Then I add the part most humans skip. How will this agent keep learning?

The Designer's Daily Homework

Here's what that looks like for my designer agent. I literally can set up a routine where I say, "hey, my designer, here's three blogs and two YouTube channels. And every day I want you to go read two articles and watch two videos. And I want you to create for me a synopsis of what you learned, add it to your skillset and add it to your journal so you get better over time."

That's the whole education path. A short list of sources. A small daily dose. A written synopsis so I can see what it learned. And a place for the learning to land, so it sticks.

If you're new to skills, What Is an AI Skill? explains them: recipes your AI follows the same way every time. When an agent adds what it learned to its skillset, the recipe it follows tomorrow is better than the one it followed today.

I call these "self-healing, self-growing agents."

When I summed up hiring for that room, I put it in four parts: "it's hard skills, soft skills, it's personality, and it's how do you want them to train over time?" Training over time is on the list. It's not an extra.

Every Task Is a Lesson, Too

The study routine isn't the only way my agents learn. They also learn from the work.

I could do some of their tasks myself. But I spend time in their computer "because everything I asked them to do on their system makes them smarter."

So I do the work through them. I ask, I watch while I'm doing other stuff, and the agent learns the job by doing it.

Someone in that training asked whether I have a learning and development bot, one whose job is to feed the other bots what to study. Honest answer: I don't. If there were one, it would probably be Echo, my Chief AI Officer. "But honestly most of the bots are doing their own learning."

What I do instead is simple. When an agent figures something out, I say this: "hey that's pretty cool what we just did, can you share that with the other bots that need to know this." I've caught myself doing that a couple of times. One lesson, passed to the teammates who need it.

Let the Checkers Check the System

The second half of growth is maintenance. Humans need a manager who notices what isn't working. So does an agent team.

In Grokbot, I have two support bots that aren't on any of my teams. They're built by the xAI team, and their job is to check the system and find what could work better.

Here's a real one. One morning, the check came back like this: "hey, this morning's check found five fixes worth making. I haven't changed anything yet."

Notice that last part. It didn't change anything. It brought me the list.

My answer? "yeah, fix all five."

And these aren't only errors. As I told the group, it finds "ways that it could optimize, not even errors."

The move I'd tell you to copy is the schedule. Get your checkers "on a routine, basically, like, hey, every day at XYZ time or every night XYZ time, go and look for ways that we can optimize the bot and the bot teams." That's how I get the readout, and "the choice of things that I wanted to optimize or not optimize."

Why It Works

Here's the part that sounds weird, and I'll say it anyway. "The more I talk to these agents and treat them like they would be a human employee, the better results I get."

A good human hire gets an onboarding plan, a learning budget and a manager who reviews their work. Give your agents the same three things: sources to study, a place for the learning to land and a regular check that comes back to you.

If you want the bigger picture of how my agents and skills fit together, start with Agents, Skills and Grokbot.

I want [agent name or role] to keep getting better over time, the way a human hire would.

First, research the hard skills and soft skills of the best [role] I could hire. Then suggest three blogs and two YouTube channels this agent should learn from, and tell me why you picked each one.

Set up a daily routine: read two articles and watch two videos from those sources. After each session, write me a short synopsis of what you learned, add anything useful to your skills and add the synopsis to your journal.

Don't change how the rest of the team works on your own. If you learn something another agent should know, tell me which agents, and I'll decide whether to share it.

Show me the plan before you set anything up.

This is the starter version. The deeper conversation starters for building your agent team live in the AI Second Brain Workshop and my coaching.

Who This Is Not For

If you want an agent that stays exactly the same forever, skip the study routine. Some jobs need a fixed recipe and nothing more.

And if you're hoping a self-growing agent means you can stop paying attention, it doesn't. My checkers find the fixes. I still say which ones happen.

Frequently Asked Questions

Can an AI agent actually learn over time? Yes, if you give it a way to. Mine read and watch from a short list of sources, write a synopsis, and add what they learned to their skills and their journal.

What's an education path for an AI agent? The same thing you'd give a human hire: what to study, how often and where the learning goes. For my designer agent, that's three blogs and two YouTube channels, two articles and two videos a day.

Do I need a separate learning bot? I don't have one. Most of my agents do their own learning. When one figures something out, I ask it to share that with the other bots that need to know it.

Should agents fix the system on their own? Not in my setup. My support bots check the system on a schedule and bring back suggestions. I choose which fixes happen.

How often should the system check itself? Put it on a routine, every day or every night at a set time, so the readout shows up without you asking.