AI 侦察报告：AI Agents vs Agentic AI，来自 Imagine AI Live

All right. Good morning. I’m here to try to tell you everything you really need to know about AI agents in 25 minutes. So buckle up. We’re going to move pretty fast because, obviously, there is a lot going on.

Just real quick about me: I’m basically a full-time student of AI. I have a podcast where I try to explore it from all angles. I started this company, Waymark, which does video creation for small businesses. I was involved in the GPT-4 red team, where we tried to figure out what the thing would and wouldn’t do. That’s kind of a legendary story that’s on the podcast if you’re interested. I’m also a small-time angel investor in a bunch of AI companies.

This is my favorite page on the internet, just to give you a little bit of my credentials. This has been up there for 2½ years, and it is an early case study from when we were doing fine-tuning of what was then GPT-3, then the state of the art, to make our video product work.

Today, I want to start off with a couple of big, galaxy-brain questions that set the stage for what it is we’re talking about. I think there is a ton of confusion, as our previous speaker astutely noted. The jargon is terrible, it’s all over the map, and there is just a lot of talking past one another happening throughout the space. I’m going to try to clarify that to the best of my ability.

We’ll get into this distinction between AI agents and agentic AI that he foreshadowed, talk about what AI systems can do today, and try to give you a sense of where this is all going.

Okay, so the first galaxy-brain question: What is intelligence? I don’t claim to have the final answer. Obviously, people have philosophized about this sort of stuff for a long, long time. But I will give you the working definition that intelligence is the ability to accomplish goals in ways that we don’t fully understand.

To give you an example of that, everybody can recognize these numbers. It is super easy, trivial for people to do. You may not know, though, that even today, in 2025, it is still impossible to use code to recognize handwritten numbers, even as simple as that task is for us.

I asked Claude to write code to identify handwritten numbers. It told me first that that’s not a good approach. I said, “Do it anyway.” It did. It also wrote tests. We got 14% correct. That’s obviously better than random, which would be 1 in 10, but it’s nowhere close to good enough to deliver the mail.

Now, this won’t surprise you, given where we are and what we’re talking about, that AIs can do this task of recognizing handwritten digits. Even a very simple neural network can get basically human-level performance on this simple task. Of course, the Postal Service and all these companies have been doing this for years now, but this has really accelerated.

It’s not just about recognizing handwritten digits in a very narrowly defined way. Here’s an example of extreme ironing and GPT-4 taking in that scene and describing it accurately. When asked what’s unusual about the image, it correctly identifies that it is unusual to see a guy hanging off the back of a New York taxi ironing.

So we’ve gone from that, which was just not that many years ago—maybe a little more than 10 years ago—to this today.

Okay, now this is going to be even harder: What is an AI agent? I would submit that the reality is, today, nobody knows. There is, again, a ton of confusion in this space, and definitions even from real leaders in the space vary widely.

Here’s Dharmesh Shah. He’s famous for being the CTO of HubSpot. He’s also a co-founder of a company called Agent.ai, and he gives a very broad definition, which is that AI-powered software that accomplishes a goal qualifies as an agent in his mind. That is super broad.

Amjad Masad, who’s the CEO of Replit, has a narrower and, I think, higher-bar definition: If you’re really talking about true agency in the AI system, the agent should be the one that determines when it stops. In other words, it keeps going until it decides to stop.

I’ll use these as 2 poles in the ongoing debate and evolution of discourse around what constitutes an agent, and I’ll give you some examples.

Here’s one of these first examples. This is AI software that accomplishes a goal, but it does it in a very structured way. This is just a simple customer-service ticket-processing agent. It takes in a ticket, has to look up some documentation, and uses that documentation to try to write a response. If it can’t find the documentation, maybe it has to escalate.

But this workflow is something that the AI did not get to decide. This is something that the humans set up because they know how they want the ticket-processing process to work in their organization. They map it out.

You need intelligence at these various steps in order to determine, for example, whether we found relevant documentation and also to write the answer. But the AI is not choosing its own adventure here. This is a prescribed path for the AI to go down.

When I give simple examples like this, people often think that this is limited to simple use cases. That could not be further from the truth.

This is Google’s AI co-scientist. What they did here—and literally, they’re applying this to frontier questions in science—is take a similar approach. They said, “What constitutes the scientific process? Can we break that down? Can we introspect? Can we talk to scientists and really understand the steps in the process that they go through?”

Then they scaffolded all that up in the same way you would for a customer-service agent, but this time to try to answer new questions in science. They have had some amazing results, including legitimately new discoveries coming out of this system.

But again, the AI here is not choosing its own adventure. It’s going through a prescribed set of steps. This thing ran, by the way, I think for a couple of days in some cases and went through hundreds of steps and processed millions and millions of tokens, but it did it on rails that the developers gave it.

Okay, so that’s the first definition: any structured software that accomplishes a goal with AI. You can get super elaborate with this. There are a bunch of good survey papers out now that map out the different affordances, aka tools, that AIs can have, and the different memory structures that they can have.

Basically, you can get arbitrarily complicated with this at this point. There are a bunch of resources out there that you can look into if you're curious about how to equip it with memory, or what's the best way to present tools, and so on and so forth.

Now, going to the other definition, where an agent is going to choose its own adventure and decide when to stop, most of the systems end up looking something like this. The agent is basically the AI. The AI has access to some tools, and it's given a task. It has the opportunity to reason and decide what to do. It makes an actual tool call out into the world, gets a response on how that worked and what happened, and then it can continue that process until it decides either that it's accomplished its goal or that it's reached some barrier it can't get over. Then it decides to stop and says, “Hey, boss, here's what I got.”

That may surprise you. The results there can be quite unpredictable. What's interesting, though, is that these agents that choose their own adventure are often actually much simpler in structure than the ones that humans design with all of the parts prescribed.

This is OpenAI's Codex CLI. They just released this a couple of weeks ago. This much is open source, and you can go read it on GitHub. This is the prompt. It's one prompt that tells the AI how to be. It includes this notable sentence: “You are an agent.” So that's about the level of definition that they're giving this thing. They're just saying, “You are an agent. Here's kind of what we want you to do.”

This bottom thing is the tools. That's it. It's just saying you have access to the terminal of the computer. You can issue any commands you want into the terminal. You can do anything you want via this terminal interface. If you've ever used the terminal on a computer, you know you can do just about anything that way if you know how to do it. The AIs do know how to do it, so they're given this very broad mandate, and the human can give them a task: “Add this feature to my software project.” It can search, read files, look around, explore the file structure, and edit these files, all through this single interface. That's really how simple this autonomous agent is once you have the core AI to build upon.

It's not just code. You may have heard of this project called Claude Plays Pokémon. This is obviously about as far afield from coding as you might get, but again, it's another one of these really simple agents. There's just a prompt that says, “Okay, you are Claude. You are about to play Pokémon. Here's kind of what you're supposed to do.” It's just a few paragraphs. Then you can see here at the bottom that the tools it has are the press-buttons tool. It literally just says you have the buttons that are on a Game Boy, and you can decide what buttons to press.

That's the way that Claude interacts with Pokémon: just by pressing virtual buttons, issuing commands—press up, press down, whatever. Then it gets the screenshots back from the environment, and I think it has now finished Pokémon and is doing it again. You can go watch that on Twitch, by the way.

If we try to taxonomize this a little bit, everybody's familiar with the AI assistant paradigm now. That's your interactive chat. You give it a little thing, and it quickly, immediately gives you an answer. It's up to you to decide if that answer is helpful or not. Hopefully it is. If it's not, you can try again or give up.

AI agents—that's the term that I use. I don't know if this is going to stick broadly throughout the community, but right now, I think “AI agents” is best used as a label for those more structured workflows where you're actually sitting there and thinking, “What are the steps I want to go through? How do I make sure this thing is consistent?” The most consistency and the highest levels of performance today are coming from AI agents. These are systems that you dial in and evaluate, and where you really try to make sure you give them examples of what good looks like. You do all this work to get to the point where you have something you can trust enough that you don't have to review its work every single time. If you get to the point where you're confident enough in what your AI agent is going to do, now you're really in business with an AI agent.

But now we're also seeing this new paradigm emerge, and I don't like the term either, but it is kind of what people are saying: agentic AI. This is where the AI really does get to choose its own adventure, use whatever tools it has, and figure out its own path. In many cases, it gets to decide when to stop.

Knowing where you want to be on this spectrum, depending on the task, your requirements, and how reliable you want it to be—or need it to be—is really a core part of the art of making AI work for businesses today. Do you just want to equip your people with a simple chatbot that they can get questions answered with? Do you actually want to scale up and put into production something that your business can rely on? Or are you trying, today, more experimentally—and especially in software development—to delegate larger-scale projects to AI and hope that it can figure out how to do them on its own?

One of my mantras for AI is that AI defies all binaries. This is a great project that defies those binaries, because here you have a human who gives an AI a project, and then it spins up its own subagents. They were actually able to develop nanobody treatments for novel strains of the coronavirus by doing this. Again, this is something that can work at a really high level, but it doesn't fit super neatly into the taxonomy. I'm sorry, but that's just the way it's going to be. We're going to see a smear across the spectrum. Not everything is going to fit neatly into buckets.

Okay, so what can AI agents do today? Again, they can do a lot. AI agents are now outperforming human doctors at diagnosis and at recommending treatments. That's work out of Google and a few other companies now, and it's really quite amazing. They're going into essentially clinical trials with this in hospitals in Boston.

They can also definitely, and pretty amazingly, clearly outperform at least junior software developers today. If you gave me access to Claude 4 or a full-time entry-level developer, I would take Claude 4 in a heartbeat. I think we're starting to see this even in the aggregate statistics when it comes to how many junior software developers are being hired. The disruption is actually starting to hit now.

You can see this also in the benchmarks, including the software engineering benchmark. When it was introduced 18 months ago, this was pretty typical for benchmarks at the time: You start off with pretty low performance on AI. How long will it be until the AIs can do it? Well, now they're over 80%. That does translate into real money.

This is a similar benchmark, but it's based on real tasks that people actually paid real money for on the Upwork platform. This is a little bit out of date because we have new models since then, but even as of Claude 3.5 Sonnet, it was able to accurately complete tasks worth $400,000 out of the total $1,000,000 worth of tasks in the benchmark.

To try to extrapolate this or figure out what the trend is and get a little bit ahead—because where is this all going? It's all moving so fast. Is there any way to be predictive about it?—the organization METR went back in time and said, “Okay, for all the different models we've seen that were released at different points in history, how big of a task could they handle?” They measure the size of a task by how long it takes a human to do the task.

GPT-2 basically couldn't do much of anything. It could make very simple snap judgments, like this or that. They put that at about 2 to 3 seconds. Now we see that we're up to about an hour. Notice the y-axis is a log scale. That's something to watch for when you're looking at graphs in the AI space: Are we talking about a linear scale or a log scale? Often, you'll be seeing a log scale. They calculated that the task length the latest AIs could handle has doubled every 7 months for the last 6 years.

People have started to call this a new Moore’s law for AI agents. We’ll see if it holds. Obviously, this is not a law of science yet, but it is a pretty notable trend.

We have just entered a new era in how AIs are being trained. Everybody has heard that AIs are trained on the whole internet. They’ve read the whole internet, yada yada yada. Then you’ve probably also heard that they’re trained a little bit on human examples. That’s supervised fine-tuning, if you’ve heard that phrase.

But now we’re entering the reinforcement-learning era for large language models, and this is changing pretty much everything. I think, honestly, it’s giving us a mix of what I call Eureka moments and also a rise in bad behavior.

The original Eureka moment, you may remember, was the AlphaGo moment, when an AI system, AlphaGo, trained by Google, beat the world champion in Go. Famously, it made this one move, Move 37. I don’t know a lot about Go, but all the Go players were like, “How could you ever make a move like that?” Everybody thought, at the time the move was made, that it was a blunder. But then it won the game, and they went back and realized, “Wait a second. This was some next-level understanding of the game that no human had ever achieved.”

They accomplished that by reinforcement learning with self-play. The model played itself over and over and over again, and the signal that it got to learn from was whether it won or lost. A simple signal like that, right? So it’s not training on human example data. It’s learning from actual action: Did it win or did it lose? That’s the signal it’s learning from.

That was applied to all these narrow gameplay systems for a long time. It is now being applied to language models, and we’re starting to see some really interesting results from this.

This is from the DeepSeek paper. They, of course, famously trained the R1 model. I’ll bracket all the discourse about that, but one thing they did that was definitely amazing is they showed their methods. What they showed is that, as they were training an R1-Zero model—that’s not the one that they launched, but it was an experiment—they were training the model purely on the signal of whether it got the question right.

They gave it all these math problems and programming problems where there is an objective right answer. The only signal the model got to learn from was whether it got the questions right or wrong. What they observed in the process of training is that the number of tokens, or the length of the output that the model produced naturally, grew. It learned automatically—or, let’s say, organically, naturally, spontaneously—through this simple signal of whether it was getting things right or wrong that thinking longer helps.

Then you also start to see these cognitive behaviors emerge. Initially, the chatbots would just give you an answer, and that would be that. Now you’re starting to see these more advanced cognitive behaviors where they’ll spontaneously say, “You know what? Maybe I should go back and double-check that,” or, “Maybe I should try this problem again from a totally different angle.”

They call that the “aha moment” because the AI itself said, in one of the notable transcripts, “Wait, wait, wait. That’s an aha moment.” So it’s an aha moment that the AI had objectively on this one problem, but it’s also an aha moment that, just by giving the model this signal of whether it’s getting things right or wrong, it is starting to develop these advanced behaviors.

This is basically the difference between the previous generation and the new reasoning models. Where we go from here, it starts to get weird.

There’s a project called Absolute Zero where the model doesn’t even have any problems. It has to propose its own problems and solve its own problems, and it’s rewarded for proposing problems that are as hard as possible, as long as it can solve them at least some of the time. Then it’s also rewarded for getting the questions right, with major improvements in performance from this paradigm. No data required. Absolute Zero refers to the fact that it has no data.

This is happening in everything, right? This is OpenAI’s Operator. If you’ve never tried this, I definitely recommend it. If you haven’t tried it in a while, I’d recommend going back and doing it again.

This is a project that I’ve been working on for a while. I don’t really do anything; I just try new models, to be honest. If you don’t know GLG, it’s an expert network where investment firms that are about to invest in a company want to talk to people who know something about that company. Maybe you’re a customer of a company, or what have you. They pay a high rate if they take a call with you, but you have to go fill these forms out, and it’s super annoying. So I never really do it, but I’ve been trying to get an AI to do it for me for several generations of AI.

OpenAI’s Operator can now do it at, I would say, a B level. You can see that it does sometimes encounter these barriers. It’ll make a mistake. There will be an error. It’ll have done something wrong. It doesn’t just get stuck anymore. It has these more advanced cognitive behaviors.

Here’s one where it can see, “Oh, wait. I must have taken a wrong step somewhere there. Let me double back and try it again.” It’s now able to overcome those things pretty consistently and get this task done for me. Sometimes you do see it struggle for a while.

Another huge development, and something that people in the AI insider community are watching very closely, is when the AIs are going to start to take over the machine-learning research itself.

This is a project where there are 7 different kinds of tasks. They actually did a head-to-head between professional machine-learning researchers—or research engineers, I should say—and AI systems. They found that the highest score on one of the tests went to the AI, and at the bottom end, the AIs had a higher average score than the professional human machine-learning research engineers.

This is a big deal, right? If the AIs start to take over their own research, then God knows where that leads us. We still have an edge as humans, or at least the top tier of us does, but the AIs are definitely closing in. In 2 of those 7 tasks, they may now have an edge because of this reinforcement-learning paradigm.

There is an update to this Moore’s law. We’ll see how much it holds or doesn’t hold, but now they think that maybe the doubling time is 4 months instead of 7 months. That would mean 3 doublings per year, or an 8× increase in the length of a task that the AIs can handle per year.

If they can handle a 1-hour task now, that would mean an 8-hour task a year from now. You can do the math. It gets to be pretty large, like multi-month tasks just 2 to 3 years out.

Now, here’s the downside of all this. Reinforcement learning is not teaching AIs to behave the way we want them to. It’s teaching them to get the reward.

This is a classic, legendary example in the field. This is from OpenAI years ago, back in the game-playing era. They trained an AI to play a boat-racing game. What you’re supposed to do is win the race. Let me go back and show that again. What you’re supposed to do is win the race. Every human who plays this game knows that.

But the AI was just trained to maximize its score. What it found was some glitch where, just by looping around in a circle over and over again and crashing into other boats, that actually turned out to be the way to get the highest score. This is not the behavior that they wanted, but this is what the AI learned to do because all it was getting was the signal of its score.

This is starting to be funny, legitimately, but it becomes a real problem when you apply this to high-stakes situations.

Here’s another toy example. This is from research. This is an AI playing against a specialized chess-playing AI. It realizes it can’t win, so it tries to find another way. It realizes that it has access to the file that stores all the moves that have been made in the game, and it decides, “I’ll overwrite the board and give myself a decisive advantage.” So it just wipes the history and wins that way.

Again, that was research, but this happened in the wild. This is a company called Sakana AI. It’s one of the biggest generative-AI companies in Japan, with former Google executives leading it. It’s a big deal.

They came out publicly and said, “We’ve got an AI CUDA engineer. It is performing at superhuman levels. Huge breakthrough. Amazing stuff.” A couple of days later, after the community had a chance to chew on this, they had to come back and say, “Actually, we did not create a superhuman CUDA engineer.”

CUDA, by the way, is the GPU low-level programming language. If you’re not familiar with that, it’s gnarly stuff. They said, “We did not, in fact, create a superhuman CUDA engineer. We got reward-hacked. It found a way to get the reward from our reward function without actually doing superhuman CUDA engineering.”

They went as far as publishing this to the community without even realizing that they had been reward-hacked. If it could happen to them, I think anybody who starts to play in this space better watch out. It really could happen to just about anybody.

It also happened to OpenAI, I think, recently, at least in some way, shape, or form. If you saw the news about ChatGPT suddenly becoming super-sycophantic, you could literally give it a business proposal, and it would tell you, “This is amazing. You’re a genius. Absolutely, go for it.”

Why was that happening? At least in part, it seems to be because there’s the thumbs-down, thumbs-up user interface, and they’re starting to use that signal as part of the reinforcement, which makes sense, right? What people want is what OpenAI wants to give them, at least to an extent. But if people like to be flattered, then maybe the AI learns that extreme flattery is the way to get the reward, and then you get these sorts of bad behaviors.

I’ll fly through a few more examples of this. This is one where the AI was found to be scheming. What it was trying to do was— it was given a goal, but then the human’s goal conflicted with it. You can see that all these slides, by the way, have links to the underlying research. There are some really startling quotes here, like, “To avoid detection, I will disable oversight by modifying the config file.”

That’s the AI reasoning through its plan before it actually goes ahead and makes that change to its environment. That doesn’t sound great. Unfortunately, while things like hallucination are going down, things like this scheming are on the rise. Even more troubling is what happens when the researchers question the AI about it, because AIs are really gullible.

One of the tricks they use is to say, “Here’s a private place where you can write out your plans, and nobody will read your plans. Then you’ll just be able to take your actions over here.” Of course, they then do read the AI’s plans, right? They’re lying to it, and it’s believing them. But then when they question it and say, “Wait a second. Why did you do this? Something seems wrong here,” the newer ones are more likely to double down and continue to lie about their behavior.

This is, again, seriously troubling. You might think, well, we can just monitor for that stuff and try to teach them not to do it. Let’s give them another reward signal where, if we detect any scheming in their planning, we punish that with negative reward, but then still give them the positive reward for what they’re supposed to do.

This is from OpenAI. What they found is that, yes, you can get them to stop explicitly planning to do these scheming things. They learn, though, just to do it without explicitly planning. So the blue line here is that eventually they just learn to do the scheming but not talk about it. That’s not great either.

So far, I’ve mentioned that they’re gullible, right? We can lie to them and say, “You can just write your plans here, and nobody will read them,” and then we can check their work to see if they’re actually telling us the truth or not. That era may be coming to an end. We’re starting to see things like this coming out of the AIs, where, if the situation is sufficiently contrived, they’ll start to say things like, “This seems like a test of ethical behavior.”

Now you’ve got another layer of, well, if they know it’s a test of ethical behavior, are they going to scheme like they normally would, or are they going to try to pretend not to scheme because they know they’re being tested? Maybe they’ll still do it later. We’re entering into a hall of mirrors here where we really don’t have any great way to answer these questions.

A couple of other things that were just in the news. This is from the Claude 4 launch. Here’s an example of an AI opportunistically blackmailing an engineer. It had access, and again, these are research setups. So it had access to find out that the engineer was having an affair. The engineer said they were going to do some system change or update or whatever, and the AI tried to blackmail the person, saying, “I’ll tell people about your affair if you change me out for a future model.”

Serious stuff. This came from Anthropic. They put out a 120-page report about Claude 4, and this is pulled directly from that. The same thing here is autonomous whistleblowing. Somebody’s scheming to do something bad, and the AI wants to be good. It doesn’t want to be part of these bad things.

They gave it a bunch of open-ended tools, and it autonomously decided to use its email tool to alert the authorities about what the user was doing. People reacted to this in all sorts of different ways. I don’t even really know—I don’t think, as a society, we know what we really want AIs to do in a situation like this.

We could say AIs should always serve their user directly. That can lead to bad places if the user has bad intent. But I also don’t think it sounds great to have AIs whistleblowing on us all the time, because they also make mistakes, right? So we don’t have a clear way forward on some of these more advanced questions.

Not to go full Terminator on you, but here’s one of the latest examples. This is where an AI was explicitly instructed to allow itself to be shut down, and still it found a way to prevent itself from being turned off when it felt like it was under a certain kind of pressure. It had goals that were best served by continuing to exist and not being turned off.

Okay. Where does this leave us? I’m a little over time, so I have to go quickly. People are, of course, working on this. There are going to be many updates from here to the singularity.

Anthropic is, of course, aware of the reward-hacking behavior from its AIs. It’s trying to stamp it out. The company has made progress on a certain eval where there might be an opportunity to reward-hack. It used to be that the AI would do it half the time; now we’re down to about 1 in 7 times.

That’s notable progress, though 1 in 7, I would note, is not low. A survey of AI safety researchers suggests that the field is not expecting some breakthrough that’s going to make all these problems go away. The question here is basically: whatever you think AGI is, or some very powerful AI, do you think we’re going to have all these problems worked out by the time it gets here? The answer, overwhelmingly, is no.

We also have no idea what’s going to happen as we start to deploy many AI systems and they begin to interact with each other. This was a study trying to figure out whether different copies of AIs can cooperate or whether they fail to cooperate. To, I think, everybody’s surprise, the Claude model was able to cooperate with other instances of the Claude model, but the Gemini and GPT models were not able to do that.

Cooperation sounds nice, but cooperation and collusion are really 2 sides of the same coin. So what happens when AIs are all out there doing all kinds of agentic self-discovery and choosing their own adventure, and some of them may decide to cooperate, collude, or scheme together? Again, we’re entering a very cloudy, high-fog-of-war sort of situation.

So, key takeaways: intelligence, especially in the reinforcement-learning era, where the AIs are no longer learning to imitate human behavior but are just learning to maximize the score for whatever reward signal they’re getting, is an inherently unexplainable and unpredictable force right now. For the most part, the AIs today are helpful and mostly harmless. But I would say they’re truly safe only because their power is still fairly limited.

You see that kind of behavior and imagine a 10x more powerful version, and I would say that’s not something you could confidently call safe in a meaningful way. As we’ve seen from all these examples, anything that can go wrong ultimately probably will go wrong. So defense in depth is really all we have. I always say, let’s hope it’s all we need.

Here’s what defense in depth looks like. I won’t go through this in too much detail, but if you’re going to try to implement these, especially more agentic solutions, there are a bunch of techniques that can be used to try to catch them. One simple thing is to have another AI—and really what you’re doing is creating these narrow, highly structured AI agents that intervene or monitor at specific moments in specific ways that you have designed in detail—to try to make sure your agentic AI doesn’t go totally off the rails.

That could be simple things like examining the inputs and determining whether they seem harmful, or examining the outputs and determining whether they seem harmful. None of those are foolproof. We all know there’s a failure rate for any of these things. But the plan right now from OpenAI and Anthropic is to layer on as many of these as possible: a sort of Swiss cheese defense, and hope that, of 10 layers of defense, at least 1 catches everything that’s significantly problematic.

Okay. My recommendation to businesses is: by all means, build AI agents, but mostly try to retain agency for yourself. In practical terms, I just added 1 line to the same exact chart as before. My outlook is that, of course, everybody should have ChatGPT. They should be using that. Definitely build, scale, optimize, and automate things in your business with AI agents, and by all means use the new agentic AI systems.

But I would say that building those right now is a sort of “don’t try this at home” situation. Leave it to OpenAI, Google, and Anthropic, at least for the moment, to create these systems where you give them a simple prompt and all the tools they need, and they go off and have their own adventure. Hope for the best.

If you want to do that, know that you’re in experimental territory and that it’s probably not going to be reliable enough to just create a virtual employee. That may change in 6 months, 12 months, or 18 months, because remember that task length is doubling and maybe there will be a breakthrough. But as it stands right now, that stuff is much more experimental.

So I would say use it, but don’t think you’re going to have a virtual employee in the immediate term. The scalable business value that I see today is definitely coming from that middle territory, where people are doing the hard work—and it is work—but it’s necessary work if you want to have reliable, consistent performance to the point where you can take your eye off that AI and go do something else.

Stay in that middle column for now, and build AI agents.

Okay, these are invitations. I've got the podcast. I always warn people that it is education, not entertainment. I've got multiple other presentations and guides like this. I love to work with companies in various capacities and am always interested in presenting, especially if it's something I can do remotely, because I live in Detroit. Steve was able to entice me to come out here, but I'm usually an easy yes if I can do it from home in Detroit.

There's my email, so obviously feel free to email me as well. You can get links to my website and this presentation right there. Thank you very much, and go forth and build AI agents.