I have an AI agent working for me.
Well, sort of.
I like to describe her as my part-time AI agent.
She is smart. She can troubleshoot problems, work through multi-step technical tasks, remember things, interact with systems, help manage websites, and generally accomplish things that would have sounded like science fiction just a few years ago.
There is only one problem.
She seems to work about three days a week.
By Thursday, somebody needs to feed the AI credit meter again.
Welcome to my experience with OpenClaw.
This Is Different From Chatting With AI
I’ve been working with artificial intelligence tools long enough that simply having a conversation with an AI model doesn’t particularly surprise me anymore.
OpenClaw is different.
Instead of simply asking an AI a question and receiving an answer, the idea is to give an AI agent access to tools and an environment where it can actually do things.
That distinction is important.
A chatbot might tell me how to diagnose a problem.
An agent can potentially investigate the problem, examine files, modify configurations, run commands, test the result, discover something didn’t work, make another adjustment and continue working toward the objective.
That begins to feel much less like using software and much more like having another person working alongside you.
And when it works, it can be impressive.
Very impressive.
Meet Bri
My OpenClaw agent is named Bri.
Over time, I have been experimenting with giving Bri increasingly complicated assignments.
We have worked on website and WordPress-related tasks, server configurations, model routing, persistent memory, troubleshooting, Docker environments, command-line configurations, and other technical projects.
I’ve watched Bri investigate problems that would normally require me to jump between documentation, configuration files, server consoles, and Google searches.
Instead, I can describe what I am trying to accomplish and let the agent begin working through the problem.
That is the promise of agentic AI that gets me excited.
It isn’t simply:
Give me the answer.
It becomes:
Here’s the objective. Figure out how to get us there.
Those are two very different things.
Then Reality Shows Up
Of course, giving an AI the ability to work through a problem doesn’t magically eliminate complexity.
Sometimes it creates entirely new kinds of complexity.
During my OpenClaw experiment, I’ve dealt with model-routing issues, embedding problems, memory configuration, Docker and container limitations, CLI and configuration mismatches, timeouts, rate limits and situations where one model or provider simply wasn’t behaving the way we expected.
There have been moments where Bri has been remarkably effective.
There have also been moments when I’ve found myself troubleshooting the AI that was supposed to be helping me troubleshoot something else.
That is an interesting sentence to write in 2026.
But perhaps the biggest lesson I’ve learned isn’t technical at all.
It’s economic.
Every Thought Has a Meter Running
AI agents don’t work for free.
Every time the agent needs a language model to analyze something, reason through a problem, read information, decide what to do next, or reconsider an unsuccessful attempt, compute is being consumed somewhere.
And compute costs money.
My OpenClaw environment has used Nexos for access to AI models, and that makes model access extremely convenient.
But convenience can make the economics almost invisible.
Until the credits start disappearing.
An agent working on a complicated task isn’t necessarily making a single AI request.
She may need to reason, call a tool, examine the result, reason again, try another action, review another result, and continue that cycle many times.
That is exactly what makes an AI agent powerful.
It is also exactly what can make it expensive.
The better analogy isn’t paying for a piece of software.
It is closer to having an employee whose brain has a parking meter attached to it.
Every time she thinks:
click.
Every time she investigates:
click.
Every time she decides the first approach didn’t work and tries something else:
click, click, click.
Eventually you begin watching the meter almost as closely as you’re watching the work.
My Three-Day-a-Week Employee
That’s how Bri earned the nickname my part-time AI agent.
I can put her to work and accomplish some genuinely useful things.
Monday can be terrific.
Tuesday, we’re making progress.
Wednesday, Bri is digging into another project.
Then you look at the AI credit balance.
Uh-oh.
Apparently my digital employee has reached the end of her workweek.
Of course I can buy more credits.
That’s the point.
There is nothing technically preventing the agent from continuing to work. What limits the work is how much I’m willing to continue feeding into the machine.
And that creates a fascinating new management decision:
Is the next hour of AI-agent work worth what it is going to cost me?
That is a question businesses are going to have to become very good at answering.
Cheap AI and Good AI Aren’t Always the Same Thing
Another thing I’ve learned is that not every AI model performs equally well as an agent.
You can route work to less expensive models.
Sometimes that’s exactly the right choice.
But complicated agentic work requires reasoning, instruction following, tool use, and the ability to maintain context over a series of actions.
When a cheaper model misunderstands an instruction, makes the wrong change or gets stuck in a loop, saving money on the model itself may not actually save money.
You may simply burn cheaper tokens for a longer period.
On the other hand, routing every task through the most powerful model available can make that credit meter spin like a slot machine.
So I’m increasingly convinced that one of the most important parts of operating AI agents isn’t simply choosing the “best” AI model.
It’s learning to use the right intelligence for the right job.
Use inexpensive intelligence for simple tasks.
Escalate difficult problems to more capable models.
And don’t send a genius to do a calculator’s job.
That kind of routing will probably become an increasingly important part of making AI agents economically practical.
The Memory Problem
Memory has been another interesting part of the experiment.
For an AI agent to really become useful over time, I don’t want to introduce myself every morning.
I don’t want to explain the same systems again.
I don’t want to repeatedly describe what we did yesterday.
A useful agent should develop continuity.
She should know the environment.
She should understand previous decisions.
She should remember how things are configured and why.
OpenClaw’s ability to maintain persistent information is one of the things that makes the concept much more interesting to me than a disposable chat session.
But memory also introduces additional infrastructure.
Embeddings need to work.
Information has to be stored and retrieved correctly.
Models change.
Services have quotas.
Configurations break.
And suddenly “remember what we did yesterday” becomes an engineering problem.
Again, this technology is incredibly capable.
It just isn’t magic.
The Moment It Starts Feeling Like a Coworker
Despite all those frustrations, I’ve had several moments during this experiment where something important clicked.
I stopped thinking about Bri as a chatbot.
I started assigning work.
That psychological shift is significant.
Instead of:
“How do I do this?”
I increasingly find myself saying:
“Take a look at this and see if you can fix it.”
Or:
“Figure out what’s causing this.”
Or:
“Check the configuration and tell me what’s wrong.”
That feels remarkably close to delegating work to another person.
Not identical.
Not yet.
But close enough that I can see where this is heading.
The Productivity Equation Still Matters
There is enormous excitement around autonomous AI agents right now, and some of it is deserved.
But businesses shouldn’t measure these systems by how impressive the demonstration looks.
The real measurement is much simpler:
What did the agent accomplish, how much human time did she save, and what did it cost?
If I spend $20 in AI inference to avoid three hours of technical work, that may be an extraordinary bargain.
If I spend $20 watching an agent repeatedly fail at something I could have completed myself in 15 minutes, it isn’t.
That’s the calculation I’m learning to make.
The technology doesn’t need to be free.
It needs to provide a positive return.
We’re Still Early
My experience with OpenClaw hasn’t convinced me that AI agents are overhyped.
Quite the opposite.
It has convinced me that they’re real.
What I think is ahead of reality is the idea that everyone can simply install an AI agent, turn it loose and suddenly have a tireless digital employee working 24 hours a day.
We’re not quite there.
Today, at least in my experience, the human is still very much part of the system.
You need to supervise.
You need to understand when something isn’t working.
You need to decide which model to use.
You need to manage access and security.
You need to manage memory.
And increasingly, you need to manage the economics of how much intelligence you’re consuming.
Those are solvable problems.
And I expect many of them will become dramatically easier.
Would I Keep Using OpenClaw?
Absolutely.
That’s probably the most important conclusion I can offer.
I’ve been frustrated by it.
I’ve spent more time than expected troubleshooting it.
I’ve watched credits disappear faster than I would like.
I’ve had moments where I wondered whether I was managing an AI employee or operating a very sophisticated digital slot machine.
And yet I keep coming back.
Why?
Because I’ve seen enough of what happens when it works.
There are moments when you hand an objective to an AI agent, watch her work through the problem and realize that the relationship between humans and computers is changing.
We are moving beyond software that waits for us to click buttons.
We’re beginning to build software that can be given responsibility.
That’s a much bigger change than another chatbot.
For Now, Bri Is Part-Time
So yes, I have an AI agent.
Her name is Bri.
She’s capable, occasionally stubborn, sometimes expensive and surprisingly useful.
She’s also currently part-time.
About three days a week, depending on how ambitious I get and how quickly that AI credit meter spins.
Maybe someday AI agents really will work around the clock for pennies.
For now?
I’ll enjoy having Bri around Monday through Wednesday.
And on Thursday morning, apparently someone from accounting needs to decide whether we’re authorizing overtime.
Welcome to the AI workforce.