Ep 850: Agent Risk, Security, and AI Sprawl in 2026: Why AI That Acts Changes Everything (Start Here Series Vol 9)

Welcome to the Everyday AI Podcast. My name is Jordan Wilson, and for the past 3 and a half years, we've put out more than 800 episodes. Yet one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI Podcast? And that's why we started the Start Here series. And with fall now back in full swing, the Everyday AI Podcast is going back to school and playing back the entire Start Here series. From front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights. Day by Day, the series is a culmination of spending more than 10,000 hours covering generative AI over the past 3 and a half years. So you don't want to miss a single episode of the Start Here series. Let's get into it.

There's always been AI risk. But in the early days of large language models and chatbots, that risk was like Bill getting something wrong in the blog post or Deborah putting up a hallucinated stat in the onboarding guide that was littered with em dashes and delves. But AI risk today is a legit different ballgame than risk was 3 and a half years ago. I mean, heck, AI risk today is unrecognizable from what it was 3 and a half months ago. And that's not an exaggeration because after hearing for like 5 years that we're 6 months away from real AI agents, well, it Finally happened. And it was actually this perfect storm of multiple events that led to unexpected business scenarios that the corporate world now faces today. You either get on board with AI agents quickly or get left behind, but do it too quickly and you could go under. The risk, the security, and the sprawl are real. And so we're going to tackle it all today on Everyday AI, the Start Here series edition. All right, well, welcome to Everyday AI. My name is Jordan Wilson, and if you're new here, this thing's for you. It's your daily livestream podcast and free daily newsletter helping everyday business leaders like you and me keep up with all the news. Like 1,000 new agents a day. What do we do? What do we try? Well, tune in. I tell you and help you make the right decisions to grow your company and your career. So after 700+ episodes, I realized I couldn't answer the most common question that people had for me, like, Jordan, you have a lot of episodes. Where do I start? Well, that's why I created the Start Here series. So the Start Here series is the essential podcast series to both learn the AI basics and to double down on your knowledge. So this is volume 9, and you can go listen to all of the episodes in our Start Here series if you just go to starthearseries.com. So that will give you free access to our Inner Circle community, and it'll put you right in the Start Here series space. So you can go listen to all of them, watch them, read about them all in one place, interact with others who are doing the same. All right, so one other thing that you need to do before we get started. My gosh, y'all, you, you have to go listen to these, episode 712 and 713. That is our 2026 AI Prediction and Roadmap series. That's like a culmination of a thousand hours of work over the years, uh, or over the past year, uh, to give you guys the blueprint for 2026. So make sure you go listen to those. All right. Uh, if this sounds kind of like our last episode in the Start Here series, not exactly. Uh, so make sure you go listen to that one if you didn't already. So this is more of just the state of AI agents, where they are, what they are, how we should use them, should we use them? So make sure you go listen to, uh, Volume 8 from yesterday. That's episode 717. But today we're here to talk about The other side, the risk, the security, and the sprawl that's changing everything. So here's what we're going to be covering on today's show and, well, why it urgently matters. Because AI models didn't just get smarter, they got hands, right? The risk model changed when AI moved from generating text like it was 3 and a half years ago to now it's taking Real actions. And a lot of times, actions we're not aware of. And that's the scary part. And an agent connected to your email and calendar or, you know, your company's data can act fast, confident, and wrong in the same way that AI models hallucinated 3 years ago. Well, they can still hallucinate now. And this isn't a future concern. This is happening right now. So on today's show, we're gonna go over a simple mental model for why agent risk is fundamentally different from chatbot risk. We're gonna talk about what OpenAI, Google, Anthropic, and Microsoft are actually building to address this risk. And I'm going to give you a practical Monday morning playbook that you can start using this week to address the risk, security, and sprawl. Sound good? Yeah, sounds good to me. I'm excited and I wrote all this. All right, so let's get a quick little catch-up here. So probably if you're listening to this show, AI's not new to you. But let me just give everyone the briefer here, right? So essentially from 2022 to mid-2023, early 2024, large language models were largely text systems, right? With the risk was just limited to misinformation and data leaks and using hallucinations and looking foolish. But that started to change, I'd say, in the mid-2025. So That's when the models started getting, well, exponentially more capable and agentic by default. That's the thing that people don't understand, right? I've actually had 2 great conversations with 2 really smart minds, kind of the head of agents at Cloudflare and then the head of Microsoft Research. And I learned a lot by talking to them both, you know, before and after the show as well. But one thing that kind of came true is, well, no one really knows what constitutes an agent. What doesn't? But I think that we can all agree that even today's large language models, they're agentic by nature, right? They, well, a lot of us are giving them access to all of our data. And not in all cases do they have right access, but in many cases they do. So when these models can think and act on their own and, you know, spin up a virtual environment and a terminal and access your computer, right? I keep saying this, even right now, I have multiple agents running on my computer, right? I have Codex and Claude Code going right now. I'll probably spin up, uh, Anti-Gravity, uh, later. I always have agents running and they have access and I don't necessarily know what they're doing, uh, you know, in between. I always go back and look when they're done, but that's what's really changed, um, in mid, uh, you know, 2025, but here we are in early 2026. And this is where now we're going to start talking about and actually putting into practice all those buzzwords, right? That people just started chatting about in like 2024 to sound super smart, like, oh, governance and audit logs and isolation protocols, right? Like all those things. Well, okay, well, good thing that everyone was talking about it because we were trying to get our buzzword bingo. Well, now we actually need it right now. It's time to talk about ethics and governance and guardrails when it comes to agentic capabilities. I like to put it like this, keep it very simple. 2022 and before, I'd say that AI was a dumb stationary brain, but it was a brain, right? So everyone was blown away like, oh my gosh, it can think. Well, it was dumb. It was stationary, didn't move, couldn't really think ahead, right? And in 2023, well, it became a dumb stationary brain with tools, right? That's when, you know, the early version of ChatGPT Plus, you know, GPT-4, it had tools, right? It could go on the internet as an example. So that's when it really started to open up what it could do, or at least the information that it could access. In 2024, Well, it was still a stationary brain with tools, but it went from a dumb brain to a smart brain. I would say in 2024 was the first time that we actually had smart models because at the end of 2024, that's when we got reasoning models. And then in 2025, I think we still had smart brains with tools, but the difference now, instead of it being a stationary brain, it was a proactive brain in 2025. It could go out at least, especially at the end of 2025, the second and third quarter. It can make moves on its own, right? Proactively could schedule things and, or just, you know, they could start acting over long periods. And then what brings us to 2026 is, well, now we have that smart proactive brain with tools and arms, right? So tools are cool, but when you have arms, You can actually use them in a real way. And I think that's where, you know, agents now have teeth when they are autonomous, proactive, and smart. So here's why agent risk, I think, feels different. Because, you know, like I said, with the chatbots, there was always risk, but was it really? I mean, yeah, worst thing you can do is you get in trouble putting out something hallucinated and you look foolish, right? Does your company go under? Probably not. Uh, right? Do you expose every single dark secret, right? It's not that bad. I mean, it is, but it's not. But that, the, the, the new agent layer, it's just a whole new type of risk that we're not even ready for. If I'm being honest, if you listen to the show, I say this a lot. I'm like, this year's going to be scary. People are not ready. Businesses are not ready. And I mean that, right? That kind of business predicament that I talked about in the opening of the show there. That is real. If you don't run to use AI agents this year, you're toast. You are literally toast. I don't care if you're a small business or a $20 billion revenue business, you're toast, right? But if you sprint too quickly, you can go under because you could make devastating mistakes. That would be highly improbable to recover from because AI agents can do things much worse than even the worst human, right? Have you ever heard stories, or maybe you've experienced this, right? Kind of a rogue employee happens too often, right? Someone's, someone's bitter maybe about getting fired or not getting a promotion and they do something absolutely crazy, right? Maybe expose all the company secrets or, you know, release some files. I don't know. Okay. That's a human. That's a single human. And you can see that human. You have eyes on that human, right? Bill in IT is watching that human. Agents are different. Agents move 100, 1,000 times faster than that one person, but you can't always see agents. And guess what? That one disgruntled employee, that's one. This isn't a video game. You can't respond 10 times. Agents can. Agents can spawn subagents like that, and those subagents can spawn like that, right? So think of in the way like a virus might spread across your computer or across the human body, right? And replicate and duplicate. It's the same thing with AI agents. A rogue employee can't do that. And that's why this risk is very different. It is very real. So I think that we spend too much time Thinking about the positives and the optimistic side of AI agents, which is great, right? Yes. Oh, now all of a sudden I have, you know, 320 AI agents, you know, doing all my work for me around the clock. Oh, that's cool. All right. But what about the risk, the security, and those problems? And more teams are experimenting now than ever before because no one's got it all figured out. And that means more sprawl and more exposure, especially if you don't have guardrails up. So here's essentially the 3 surfaces where agent risk actually lives. Number one is the input. That's kind of that untrusted content that can contain hidden instructions agents, you know, treat like real commands. All right. That I don't think is going to be as big of a deal, right? Your inputs. Don't get me wrong, things can go wrong, right? People are blindly copying and pasting. So you can copy and paste prompt injections, but prompt injections are the big thing, and we're gonna talk about that here in a second. But still, inputs, they can be poisoned, right? There's, there's things that you might not know. And in the same way, a rogue human can create a lot of rogue AI agents that can create a lot of risk and a lot of sprawl that's gonna be uncontrollable. So, you know, inputs, I think you really only have to worry about it, you know, if someone really doesn't know what they're doing or if someone is trying to be malicious, which again, those things are gonna come up. But think of that one bad employee, what they can now do if they know agentic AI, right? Yeah. Talk about malware, spyware, right? Ransomware that we, you know, these companies that have paid, you know, millions of dollars, hundreds, tens of millions of dollars for ransomware. It's gonna be way worse with agents. So the second layer is tools. And I think this is where it starts to get a little dicey in terms of, well, the capabilities are, eh, wild, right? So every permission and connector that you add expands that blast radius, essentially, when something goes wrong. In the same way that I walked you guys through the, oh, it's a dumb stationary brain, but hey, once that brain gets tools, okay, now it can start doing a lot of things. And the same thing on the agent gone wrong side, right? If it was a dumb stationary agent with no tools and no arms, it's like, all right, well, have fun, buddy, right? You're in a glass case of emotion just shaking up. When they have tools, that's where things go wrong, right? When they have access to your terminal, right, to your computer terminal, that's where things can go wrong. When they can run code on a machine, that's where things can go very wrong. And then last but not least, and this is the big one, this is actions. And this is the biggest thing that if I had to boil down the biggest change in risk and why it matters now more than ever, It's outputs to actions, right? What we had to worry about a couple of years ago from AI in terms of risk was the output. Now we have to worry about the actions, but it's actions at scale and actions that we might not even necessarily be able to see, right? Silent, unintended workflows that you may not be tracing. And really what this comes down to, well, it's this combination of increased capabilities from agents and moving in the shadows. And that's an enterprise nightmare. That is literally the formula for an enterprise nightmare. So some stats here for you, right? So right now, 57% of employees at least admit to using personal AI accounts for work. It's way more than that. Let's be honest. That's just the number that admit. A third admit to inputting sensitive data into unapproved tools, right? So your shadow AI use case there. And here's the thing, you can't govern what you can't see. And most organizations can't see their agent footprint at scale. This is what I call the 3 types of dark AI. All right, for the most part, you're not going to find, you know, the 3 dark types of, or the 3 types of dark AI online is something that I've kind of categorized them in, but I think it's really helpful to get a better glimpse and a better understanding of the categories of risk. So number one, we all know this shadow AI, right? That's just essentially unapproved or unknown AI use, right? If you've been using, you know, ChatGPT on your personal computer, because Copilot is the AI that's approved, but you want to use ChatGPT and you copy and paste things over as an example, right? That's shadow AI, but that's been around. Everyone knows that, right? Uh, what you've maybe heard of, maybe not, it's the next kind of tier, and that's agent sprawl. But agent sprawl is known, right? So that's essentially when you have approved agents, but you're not sure how to wrangle them or observe them all. You're like, oh, well, yeah, Bill gave us that, you know, agent to help with finances, but We're not really sure what it's doing, right? We think it's doing good, right? I checked the outputs, but I don't really know how it's getting there. That's, that's, that's the beginning of agent sprawl. But the thing is, agent sprawl goes quickly. In the same way, a snowball at the top of the mountain might come rumbling down 1,000 times the size. That is where we get into then dark agent sprawl. All right. This sounds like a screen name for AIM back in the '90s. You guys remember that? AIM, instant messenger, right? Dark Agent Sprawl 2026. That's going to be my username on the Everyday AI Inner Circle community. But essentially that's where there's, well, Dark Agent Sprawl can be a couple of things because Agent Sprawl, It's a problem. But like I said, for the most part, that's a known risk. And you're like, yeah, we have these agents going all over the place. We have no guardrails. We have no traceability, observability, right? It's— but you know of it. Dark agents sprawl is agents you don't know about. Those are unapproved agents that are working in or on your company. And you can't observe them because you don't know about them. So this is, well, this can be shadow AI, you know, gone agentic, right? So people plugging in agents that aren't approved and company doesn't know. But there's also another kind, right? And I think we're going to see a lot of this, maybe not in 2026, but in 2027. That's going to be the equivalent of malware or spyware, but agents, right? People sending and seeding agents out specifically in the same way that they would for spyware, Malware, ransomware to make money, right? To extract, you know, value from businesses. And that's what's going to happen. That's the next version of this. That's not necessarily dark agent sprawl, but there's 2 sides, right? Dark agent sprawl can start out innocent enough, right? Oh my gosh, you know, our company's not approving, you know, any of my AI agents. Well, okay, whatever. I'm going to go ahead and, you know, unleash You know, Claude code and get, you know, 50 instances of Claude code going up and they're all going to spin their agents. Well, one person might know, but the rest of the company's in the dark and those agents could replicate, duplicate, and you can't observe them. But the other thing is, well, bad actors, dark agents, sprawl, that's the thing too. So why is this all happening now? All these risks and security concerns and the sprawl. Why now? Because like I've said, We've been 6 months away for 5 years, right? We're 6 months away from great agents, but then it almost seems like we didn't get a 6-month warning. They just popped up, right? Like December 2025, right? Like everyone was winding down for, you know, the holidays, you know, especially here in the US. And then you go back to work in January and you're like, what the frick happened? Right? You're like, it's like, whoa, whoa, wait, the agents are actually here now? Where's the 6-month warning? Why didn't we get, get word of this in, in June or July? But there's 3 reasons why, and it is literally the perfect storm, not just the ingredients happening, but at the exact right or wrong time. All right. So number one, the number one element that was mixed in the bowl and it's exploding is the reasoning threshold, right? So improved reasonings from models like, you know, GPT-5.2, From OpenAI, Gemini 3.1, their new model, very impressive. And, you know, Opus Sonnet 4.6 from Anthropic. These are all built to be agent native. That's how they build them now, right? They're not building models that are great at, you know, reading, writing, and comprehension first. No, what they're focusing on is the harness and the tool use. That's what's first, right? Even, you know, Google's You know, their new model yesterday, everything that they highlighted was tool use, right? And how to, you know, improve tool use and the scaffolding there has, you know, really helped them improve their outputs. But that's the thing. These models, their reasoning ability is legit through the roof. They can plan steps ahead, self-correct errors, and move beyond reactive behavior into proactive. And that's kind of reduced agent reliability from around 50% to 90%. And that's perfect storm right there, right? 50% coin flip. You're not doing that, right? Would you hire an employee that has a 50% chance to fail right away? Probably not, but 90%. Okay. That's an A employee. Um, step 2, computer use improvements. This seems small. This is big. Okay. The models have to be insanely smart, right? They have to be able to think, reason. They have to have genius-level IQ, which is what today's models do, right? If you look at offline IQ tests, they're scoring in the genius level, smarter than 99.9% of people, right? But the computer use is important. Doesn't matter if it can't go and use a computer because at least for now, that's how businesses generate value. I think eventually value will be just generated agentically, right? The web has gone agentic, right? Google and Microsoft announced support for essentially an MCP version of the web. Where websites can talk to each other, agents can talk to each other. So we'll see how, you know, business value is extracted in the future because a lot of times it's been through the website stack, the software stack. But, you know, who knows what it'll be in the future. But right now, agents can use computers better than humans, right? So computer use means these models can use a mouse computer, they can click, they can use APIs, they can talk to each other. But the big thing is the computer use capability gap has essentially been solved, right? So Claude is on at 4.6, which came out, well, earlier this week. All right. So if you're listening to this in 6 months, right, it came out in mid-February. So imagine in 6 months, these computer use models are going to be insanely good. But it's the first one that scored better than humans, right? So 72.5 success rate on the OSWorld benchmark. And that's surpassing human performance for the first time and nearly quintupling the scores of 2024. That's the thing. And I think that's one of the reasons why, you know, even though the, uh, maybe the foundation was there for AI agents, uh, you know, a year, year and a half ago, they couldn't use the computer. It was so slow, uh, right? It was, you know, very, very, very slow computer vision, right? Every time if you wanted to click something, it would take 3 minutes. Okay. Now models can go as fast as humans, or at least, you know, not just clicking and navigating interfaces, but just a range of computer use tasks. And then the third thing that has created this perfect agentic storm, well, it's the context window and memory, right? So being able to work on tasks for a long period of time, right? I think my record so far, I think I got to about 10 hours overnight the other night on Codex, which was really fun to do, right? But it kept its memory persistent the whole time, right? It didn't forget what I told it when I went to wake up. You know, when I woke up, I spent time reading through the chain of thought and I'm like, oh sweet, not only did it remember anything, but I'm going through, I'm, you know, tracing it. I'm, you know, making sure it kind of stayed within the confines of the instructions I gave it. And it did. But, you know, that's another reason why it's happening. And y'all, let me tell you, If you haven't in the last like 3, 4 weeks, if you haven't went out and used ChatGPT or, you know, OpenAI's Codex, if you haven't used, you know, Claude Code or Claude Cowork from Anthropic, if you haven't used Anti-Gravity from Google, I'm not saying this to like be that guy. You're gonna get left behind, right? And if you're listening to this show, I don't want you left behind. I'm actually thinking, all right, I don't know this yet. It might, might kind of get a little bit of a vibe working focus, you know, here in the first or second quarter. So make sure if you're not already in our Inner Circle community, get in there. Yeah, I'm gonna start putting, putting some stuff in there. Just keep, keep your eyes open for that. All right. But let's keep going a little bit here as we gonna be wrapping up here in a minute. Let's talk a little bit about how the biggest AI companies are actually responding right now because the risk is well known because it has literally been an explosion and it is going to get worse. So I want to talk about a little bit about kind of the big picture focus of the Big 4 labs. So right now, OpenAI is taking more of a human approval approach, right? So Codex is kind of their command center to review agent decisions. Anthropic is really going on the defense against prompt injection for browser agents. you know, with making sure that virtual machines are isolated and have minimal privileges and going with domain allowlists versus blacklists, right? So everyone has a little bit of a different approach here. You know, Google, as an example, their Project Mariner browser agents run in virtual machines as a safety to isolate them from anything else they can kind of get their hands on. And then Microsoft, right? There's millions of ways that Microsoft is doing it and millions of ways that all these companies are doing it. I just kind of picked out you know, different aspects to illustrate their different approach. You know, Microsoft as an example with their Copilot Studio, I mean, the governance is everywhere, right? They have the Sentinel monitoring, the Purview logging, you know, the agent intra-ID, right? So Microsoft, it is very enterprise. Google is as well. I was just giving an example of, you know, how their Project Mayonnaise kind of runs in an isolated virtual machine because essentially I think From the people I've talked to, at least 3 of those 4 companies, they know risk is part of it, right? Like there's, like any lab, right? They call them frontier, you know, or AI labs for a reason. You run experiments and part of it is, well, you know that things need to go wrong, right? So these companies are intentionally Trying to get all the risk and all the security nightmares and all the sprawl they can, right? So then when they set a new model out in the wild, when they're done with the red teaming, they hope that they have a good idea. So the labs are working on this, but here's the reality. I think there's a pressure to allocate more resources to the development of models Versus research and security, right? I don't have that on, you know, privileged information. That's just from, you know, talking to a lot of people and reading a lot and just the, I think the reality of the world that we live in. And that's why AI sprawl is going to be hard to miss for most business leaders. Because everything's unclear, right? The tool, like, do you know, think of the AI model you use most. Do you know the tools it has?

Right?

Even if you think of, you know, ChatGPT, Gemini, Claude, do you know what tools the base models have? Probably not. You'd be surprised, right? Like, I'm one of those weird guys that reads changelogs and model cards. Most people have no clue, right? And this is much worse than classic shadow IT because agents can act across systems. Not just, you know, within the confines of folders and files, which are very structured. The whole kind of, it's kind of like how people talk about hallucinations, right? Oh, you know, they're not a bug, they're a feature, right? What makes agents great is also the built-in nightmare. So if you want to have the good, you have to recognize the bad that they can do. Right? And that's because that's how they're made. They are made to go build their own path. They are made to blaze their own trail, right? So by their very definition, agents aren't necessarily always good at staying within guardrails, right? Because if they think that the destination that has been given to them, at times, right? Depends on the model, the setup, all that, right? But it's very common for an agent to hop over guardrails in order to accomplish a task. Not knowing that that's a bad thing, right? And every new skill that you add, that you give to an agent or a model, every connector, every workflow, all that does is it expands the attack surface for everyone. And that's another realization that I think most people are going to struggle to grasp, right? Because you think that you're expanding The amount of work that you can get done. And are you sure? But I think you almost have to think of it from like a, I don't know, like an old school, like military perspective, right? It's your land, right? And hey, we're gonna go discover new land. We're gonna go out and, you know, win new deals, right? So we're gonna expand our land. I don't know if any of you have ever played like, you know, Risk. Um, I used to, I wish I still had time, right? It's one of those games, you know, it's like, oh, okay, this is good to make sure my brain still works.

But it's kind of like that.

Yeah.

Right? Um, your agents without you knowing though, are acquiring new territories and that's great. And you think, oh my gosh, look at these new skills and capabilities, right? It's like, oh, it's like having 1,000 new employees.

Okay, great.

But your surface area for risk attacks and sprawl is also multiplying at the same time as your capability. But you're not focused on that. You're focused on, oh my gosh, now all of a sudden I have a designer. Now all of a sudden I have a data analyst. Okay, well, what's that data analyst doing with your data? Are you going to check, right? Is it using an open source, right? This is one thing I was kind of blown away by the other day. Gave, gave, you know, it wasn't anything bad, right? Gave a model a very hard task. This is one of those that overnight I came back and I realized, oh, it downloaded another large language model locally to do the task, right? It did it locally, right? So kept all the data kind of on-prem, on my local machine. But what happens if in the future other AI models are just going to go use other AI models, but on the web, right? They can. What if they do that without you telling? Or what if they go use a site that's unsecure? And this is why I think, you know, OpenCLAW, as amazing as it is, This is why it's also scary, right? Because, you know, these, you know, autonomous open source agents, the open source, if I'm being honest, and I'm sure, you know, I'm gonna catch some flack from this, from some OG open source people, right? OG open source, I'm not talking about you. Today's open source, it's taken a weird turn over the past 3 to 6 months, right? It's almost become this, you know, crypto infused, Wild, wild web point for west, right? It's not like traditional open source anymore. So when we talk about open source AI agents, right, so many of what's— so much of what's out there is risk, right? It is, and it's unknown, and it can be scary if you don't know exactly what you're getting into. And I think this is why small agent pilots are becoming huge risk, right? Because when you build your own agents without centralized visibility or governance, that's where you get in trouble. So many people, when they're using their own agents, it's for them, right? But it can be going and doing work for the whole department, the whole company, or accessing data from the whole company. But if one person has eyes on it, and if there's not central organization, that's where you run into something. So here's the playbook, right? If you're feeling a little scared, you know what, maybe that was my intent. I got y'all pumped up and, you know, excited with yesterday's show talking about, you know, the new capabilities of agents, but you gotta get it in check, right? You have to balance that optimism with a little bit of realism. But here is the Monday morning playbook for how to deal with the risk, the attacks, and the sprawl. So like what we talked about yesterday, touched on it briefly, but you need to start with bounded autonomy.

Right?

When you think about autonomous agents, it's not, you know, 0 to 100. It's bounded autonomy. What that means is you start with suggesting, then propose, then approve, then limited execution, right? So many people go to full execution first. It's baby steps, right? A human being doesn't go from womb to sprint, right? You go from womb To, you know, being, being on your stomach to sitting to crawling to walking, right? Your agents have to go the same way. Even if you know out of the box that agent can sprint, you're not prepared for that agent to sprint. Part of it is for the humans, right? We think it's more for understanding the, the agent's abilities. It's not, right? This is to make sure that you don't have a bunch of lazy humans in the loop. And instead you have proactive expert-driven loops. So start with least privilege by default, read-only first, write access only for narrow-defined tasks, right? Especially in enterprise environments, you don't want to send out a bunch of read-only general agents. Start with read-only first, observe, improve your loop. Then you go to limiting, limited execution. And then you can finally get it to writing for narrow tasks. And then last in your Monday morning playbook is you need to require human approvals for irreversible actions like sends, deletes, purchases, and permission changes. These are all things agents can do, right? Agents can literally do anything. Agents, right? And this is going to get even harder. to manage when we start to see true agentic commerce, right? And I'm not just saying, oh, you know, my agent is gonna go buy something, you know, from the Amazon agent. That's not what I'm saying, right? There's going to be agent bartering. There's going to be agent, you know, liaisons, right? So you have to work on human approvals now, right? It's almost like you have to understand that agents are capable of, you know, a 10, and you have to give them a 2 because we as humans need to first learn to adapt our behavior before just giving in to agents. And then you need to build governance before you scale. And I think that's like just what I was getting at. We get all excited for what we know it can do. And they were like, wait, we gotta do this boring stuff first? Absolutely. Because if you don't do the boring stuff first, if you don't go through the sitting on your tummy, right? Sitting up, crawling, walking, then you can't expect to understand, uh, the path of the sprinting agent. And every agent run needs a decision trace that you can expect after the fact. So you have to have, be able to log all your tool calls, capture your decision traces, and monitor for abnormal action patterns. This is one of the reasons why I said, I think we're going to see in 2026, it's going to be extremely common to have agent op teams, right? In the same reason— or sorry, in the same vein, you know, DevOps teams are very common, right? We're going to have agent op teams for this exact reason. So here's what to watch for for the rest of 2026 when it comes to agent risk, security, and sprawl. Browser agents are going to become a mainstream risk surface. I think we know that. I think we're going to see a major OpenClaw type. I'm not saying OpenClaw themselves, but I think we're going to see a major Open source agent crash. I talked about that in the 2026 AI Prediction and Roadmap series. I actually think it's going to come from, right, there's this new trend, like trend over the last like week or two, where everyone's just sending, pointing their agents to a website, right? When they want their agent to do something, they're not even saying, all right, let me, let me, the human understand this. Let me write it out. Let me apply this to my business logic. Let me apply this with my garden. Nope. They're just saying, pointing their agent, hey agent, here's this video, go watch it and do it. Hey agent, here's this website with a whole bunch of cool stuff. Just go do it. You know, there's this whole concept now going around of, you know, point your agent to a URL. Okay. Have fun with that. Don't do that. Right. Don't. Right. Especially if that agent has its own dedicated machine. Right. And I'm saying, If you're tinkering around, right, it's your personal computer, you're the business owner. If you want to take that risk, that's fine, right? Especially if you're tinkering around, that's fine. But don't do that in an enterprise. And unfortunately, people are doing that. And that's a bad thing because guess what? A lot of those sites, quote unquote, oh, you know, someone puts up a huge tutorial. Okay. They're putting it up on their personal website. You know how easy it is to hack Uh, to phish any of these websites.

Okay.

You see, let me give you an example. Something goes viral on Twitter. It's some, you know, uh, literally some 20-year-old kid, you know, put a great, uh, you know, guide up on the website.

Cool.

And, you know, everyone's just like, hey agent, go read that. Go read that. And then you have millions of agents going to just read this, you know, this nice kid put up a cool guide. Okay. Guess what's going to happen? Someone's going to see that. They're going to hack that site and they're going to inject malicious code in there that no one's going to see. That's what's gonna happen. Alright. Next, the agent skill marketplace is going to expand the supply chain risk. So you inherit what you plug in, and we've already seen that with some skills and plugins for agents. Some of the top ones were found to have, you know, malicious code in them. You need to— we're gonna see identity and permissions becoming a board-level compliance requirement, not a nice to have. And last but not least, the winners are gonna treat agents like production software. Not side experiments. All right, that's it. That's a wrap. Volume 9 of the Start Here series. I hope this helpful. I hope this was helpful, and I hope you understand a little bit more the risk, security, and the sprawl that you need to be aware of, and how this having AI that acts, having AI right that has a smart brain, it's proactive. It has tools and it has arms. That changes everything, right? This perfect storm that has happened in the last 30 days. We've seen, I've said this before, we've seen more both on the opportunity and the risk side. We've seen more in the last 30 days than we've seen in the last probably 2 years. All right. So I wanted to take a moment for the Start Here series to address this. And I hope this helps you not only understand the risk, but if you want to take advantage of the opportunities, you can't do it without knowing the risks. So now you do. All right. So I hope this was helpful. So if so, there's already 8 other in our Start Here series that you should go check out. So like I said, please go to starthearseries.com. That is going to give you free access to our inner circle community. Once you sign up, you're going to be Uh, just, uh, straight up loving it, hopefully, uh, in our Start Here series space and connecting, uh, with now more than 1,000 people, uh, in our, uh, Inner Circle community. So thank you for tuning in. Hope to see you back for more Everyday AI. Thanks, y'all.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.