Most people think they're using AI well when they get a decent answer from Chad GPT. That was enough six months ago. It's not enough anymore.
The next shift is AI agents. [music] And the gap between people who understand them and people who don't, it's about to get very expensive. I've spent years in the boardrooms of billion-dollar companies.
And the good news here is that agents are much simpler than most people think. So, in this video, I'll show you exactly how they work. when to use them and how to start before everyone else catches up.
An AI prompt and an AI agent are completely different. But most of us are still stuck with old habits. Let me give you the simplest way to think about this before we go any further.
This is our first framework right off the bat. I call it ARR. If a task is autonomous, recurring, and reviewable, it's a strong candidate for an agent.
If it needs live judgment or it only happens once or can't be reviewed clearly, then use a prompt. That one distinction alone will put you miles ahead of most people using AI today. The internet gave us search, so we started googling.
AI gave us LLMs or large language models. But most people today still think of AI as a glorified search. Now agents are here and we're still making the same mistake again.
We think of agents as just more capable chatbots. They're not. A chatbot waits for your next prompt.
An agent figures out its next move. Prompting is like sitting next to a student driver. You still have to guide them, correct them, and stay very alert.
An agent, on the other hand, is a hired driver. You set the destination, hand over the keys, and just sit in the back seat. It handles the route, the traffic, and all the step-by-step decisions.
That's the mental shift we have to make. So, here's a prompt. Write me a LinkedIn post, and here's an agent.
Watch my [music] industry every Monday. Find the three most relevant stories. Study my previous posts.
Drft the new post based on those stories in my voice. Revise against my style, and schedule it for Tuesday morning. That's the power of AI agents.
And to wield that power, you need to know what's actually running under the hood. Everyone's talking about AI agents. Almost nobody can tell you what's actually happening inside.
A chatbot predicts the next word. [music] An agent decides the next action. Here's how a chatbot actually works.
It's a large language model. When you type a question, it's going to break that question into small units of words called tokens, and it converts them into numbers. and then it just finishes the sentence.
So if you said Jack fell down and broke his crown, now you [music] know that, but LLM does not know that rhyme. It will know words like bones and heart and crown [music] and all of those words could make sense. But based on its training, it predicts that the most likely next word given that line is crown.
It's based on probabilities. The agent has the same language model in the center, but now there are four workers around [music] it. Analyst, planner, operator, auditor.
One finds the pattern, [music] one decides the plan, one does the work, one checks the results. Let's make this real. You can give an agent some instruction like every Monday at 7:00 a.
m. review the past week's customer support tickets, sales notes, and product feedback. Identify three biggest recurring issues, summarize what changed, and email my leadership team a one-page weekly brief.
[music] That's it. I mean those are lots of steps but the agent will read the tickets notes and feedback and find the pattern analyst. [music] It will then decide what matters most and what belongs in the brief planner.
It will write and send the update operator and then it'll check for weak logic missing context or sloppy conclusions and it'll refine it auditor. Now by Monday morning the brief is in your team's inbox. You did not write the report.
You did not analyze it. You just assigned the job of four people to one agent. So here's your move tonight.
Open chat GPT agent mode and give it one recurring task. [music] But then watch what it does. You'll see all four workers show up in real [music] time.
That's the anatomy of an AI agent. Now that you understand the parts, let's look at the entire loop. The best thing about agents is that they can adapt when things go wrong.
This is what makes agents genuinely different from everything that came before it. In the 1970s, there was an Air Force Colonel John Boyd and he studied a very intriguing puzzle from the Korean War. American pilots in their F86 kept beating technically superior Soviet MiG.
Now, the MIG was faster and it could climb higher. It should have won, but it didn't. And Boyd eventually found the difference.
The American pilots could see more from their cockpits, and they could adapt faster. So, they got inside the enemy's decision cycle before the enemy could respond. He called that loop the UDA loop.
Observe, orient, decide, act. [music] And in the world of agents, it's the same thing. That is the real test of an agent.
When the obvious path fails, can it choose a better one? Can it go through its own UDA loop? So, let me give you a concrete example.
You can build an automated workflow. Every Friday, check [music] this week's grocery prices, build my shopping list, and place the order. [music] It works every Friday until one week your usual item is out of stock, [music] and you have six friends coming for dinner on Saturday.
So, that automated workflow is going to break. Not because it's dumb, but because it's designed to be obedient. is designed to not think on its own.
An agent, on the other hand, does something very unique. It sees the usual list. It sees that it's not working.
It finds substitutes. It adjusts quantities for six people. It checks your calendar, sees the dinner, and rebuilds the entire order around it.
A workflow can follow the process. An agent can reroute it completely. [music] That's the difference.
So, when someone says they built an agent, ask one question. When the first path breaks, does the agent keep following the script or can it [music] find a better path? Can it find another way?
That's the agent adapting in real time. So, this begs the question, right? If agents have autonomy, why do they still fail so often [music] in real life?
That's next. The most dangerous thing about AI agents is that they will do wrong things faster and with more confidence than you ever could. An agent is not magic.
It's a multiplier. I was working with the board and leadership team of a large consumer company. I'm still working with them.
And they're profitable, well-run, great CEO. And when I asked what was stopping them from using AI to drive customer acquisition, for example, the CMO responded, we have all the data, but we'll still need to build a clean process so we can turn that into something useful, something insightful. And I asked where the real challenge was.
And she said, you know, we need the right people in the seats first. That's the story everywhere. Most AI problems are human problems in disguise.
An agent is just a mirror. It reflects the quality of your thinking back at you. It just amplifies it.
Give an agent vague goals, sloppy directions, and no way to get feedback, and it will drive the car straight into the tree faster and with more confidence than you ever could. Here's the dangerous part. An agent doesn't fix bad thinking.
It formalizes it. Usually the agent fails because the human was vague, not because the underlying model was bad or anything. So before you automate anything, run a GPS check.
Goalp proof steps. Goal. Can I define the goal in one sentence very [music] clearly?
Proof. Can I tell what good looks like? And how will I know if the agent got it right?
And steps. Can I describe each and every step very clearly without a lot of handwaving? Unless you can do those three things very well, your agent is not going to make any difference.
I'll give [music] you an example. Here are two instructions for your agent. First one, summarize my emails every morning.
It's good. And here's the second one. Every morning at 7:00 a.
m. , read my unread emails, categorize them by urgency, draft replies to routine messages, and flag anything from my top five customers. So, you see there is a difference between those two instructions.
And that [music] gap is exactly where the mess lives. The winners who can wield the power of AI agents aren't [music] just going to be engineers. They'll be the people who understand their work deeply enough to define it [music] precisely.
Most companies want AI everywhere. The ones actually winning are obsessively narrow. If clarity is the bottleneck, then the opportunity is not broad intelligence, it's narrow ownership.
I was visiting uh the customer conference of a construction software company that I work with and the product lead was on stage showing a demo of a single agent [music] that was focused on a very specific problem collecting field data for a specific type of customers in a specific type of situation and it was a beta launch. The demo worked mostly with a few minor glitches here and there, but when he showed the QR code at the end, every hand in the conference room went up with their phones. Everyone took [music] a picture because it solved a very specific but very real pain they all had been living with for decades.
[music] That is where the real opportunity is in your career or in your company. Narrow [music] focus. Here's the test.
Find a highly specific task [music] people hate doing, but they have to do it repeatedly. That's where the money is. You know, we're entering an age where we'll have more agents than human beings on this planet.
On the business side, for every software company that exists today, there will be an agent company trying to dethrone it. The winners won't build the broadest agents first. They'll build the one that understands one workflow, [music] one market, and one kind of user pain better than everyone else.
By the way, if you want to keep this conversation going, I write a short newsletter once a week, just useful ideas, tools, honest reflections. You can subscribe below. It's free.
AI will reshape almost every role, but it won't replace what makes you irreplaceable. You know, AI is a giant decoupling machine. For most of our modern history, your income was tied to your hours.
[music] Even at the top, you're always trading time for decisions. Agents are breaking that link for the first time. Now, they do the work and you scale your judgment in areas where it matters most.
And that changes what's valuable. We're entering an era of infinite output, content, code, and analysis all becoming super cheap. When intelligence becomes that cheap, judgment becomes even more expensive.
When output becomes infinite, taste becomes scarce. Every time you define a task clearly enough for an agent to run it, you're not just training the system, you're clarifying your own standards. You're learning what good actually looks like.
Sure, some roles will be reshaped. The parallegal, the junior analyst, the coordinator, but every disruption has always created new roles that never existed before. [music] Before the internet, nobody imagined a role of an online community manager.
The question is not whether the shift happens. It is whether you shape it or get shaped by it. The most valuable person is no longer the one who can [music] think the fastest.
It's the one who can define good work, spot bad work, and know when to trust an agent and when to trust a human. That is where your value is going to move. Ironically, AI will make human life less robotic.
