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Everyday AI Podcast – An AI and ChatGPT Podcast
Ep 717: AI Agents in 2026 Explained: What They Are and When You Should Use Them
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This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.
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Whether you're newer to A.I. or if you consider yourself an A.I. expert, we're all probably thinking the same thing. A.I. agents, A.I. agents. A.I. agents. That's because, well, we've been promised that A.I. agents would take over, maybe in a good way, maybe in a bad way. Yet that promise never really came to fruition. I mean, we heard it in the early chat GPT days. 2023, 2024, 2025 was supposed to be the year of the agents, but it wasn't. But guess what? We're there. I think we've actually arrived with some of the recent AI updates from the big companies in Anthropic, OpenAI, Google, Microsoft, even crazy viral open source projects like OpenClaw. I think now the long vision that we've been pitched of AI agents is here, but it's all happening at once. Like I said, I've been doing this everyday AI thing for three years and at least when it comes to AI agent development, the last month has been as eventful as the other 2.9 years combined. Don't worry, even if you are brand new or if you're an expert, that's what we're going to tackle on today's show. We're going to hopefully get you caught up, tackle AI agents from all angles and do so in a hopefully quick, beginner friendly way. So this is the Start Here series and we're going to be going over AI agents in 2026, what they are and when you should use them. All right, I'm excited for this one. I hope you are too. What's going on? Welcome to the Start Here series. My name is Jordan Wilson and the Start Here series, well, I created it because after 700 plus episodes of the Everyday AI podcast, I didn't have a good answer for the most common question I got. People say, hey Jordan, looks like a lot of great information here on your podcast. Where do I start? Well, you start here with the Start Here series and this is volume eight in the Start Here series. But this is an essential podcast series to both learn the AI basics and to double down on your AI knowledge. And the way that you should really do that is make sure you go to Start Here series dot com. That is going to give you free access to our inner circle community in our Start Here series space. So you can go catch up on all of everything in this series. In one place, a bunch of additional resources that we've thrown together, all available for you right there. So like I said, whether you're brand new, an expert, it doesn't matter. Go join now, like a thousand people that are already in the community and listen to all of the Start Here series. Also, I have to put this out there because it's important. Make sure you also go listen to episodes 712 and 7 13. That is our 2026 AI predictions and roadmap series. You know, 26 kind of bold predictions on AI, and at least a third of them are about agents. All right, so and if you missed our last Start Here series, that was volume seven, where we went over context engineering, how to get expert level outputs from AI chatbots. But let's get straight into AI agents, kind of the state of AI agents in 2026. So here's what we're going to be going over on today's show. We're going to talk about what AI agents actually are and how they different from. How they're different from traditional AI chatbots or AI powered automation. I'm going to give you a decision framework for when agents are the answer and when they're worth it versus when they're just overkill. And when you should say, hey, that should just be a human task or an AI, you know, large language model task. And then I'm going to give you the practical path to, to safely starting your first agent pilot. Not even this year, like this week. All right, let's get the explanations out of the way. All right, so like I said, if you are, if you've been building AI agents for, you know, a decade or two, you can just skip ahead a couple of minutes. Actually, speaking of that and why I think it's so important to look at this from a foundational level. So about two weeks ago, had a fantastic guest on my podcast. She's the head of Microsoft Research. He's been working in AI agents for 20 years. And one thing we talked about both on the show and before and after is the definition of an AI agent is constantly changing. Right. So even what is an agent and what's not an agent? What can an agent do? What can't it do? It's always changing. All right, so if you're listening to this in, you know, February of 2026, that's great. All this information is probably going to be extremely fresh and accurate if you're listening to this in June or in 2027. Right. Some of this might be a little old, so keep that in mind, but I'm going to give you at least as of February, you know, 19th, 2026, this is the realest, most up to date. So what is an AI agent? In very simple terms, it's kind of like an AI chatbot, but it has tools and it has permissions and it has a goal, it, and it makes its own decisions. And sometimes it might start off on one path and say this isn't right and it'll go backwards and start down another path. So they can plan ahead, take steps, use tools, take actions, check results and retry within the guardrails, whatever those guardrails are, whether you set them up or they're set up by a certain, you know, piece of software that you're using. So what do agents do? Well, they can take on delegate work, they can delegate work to other sub agents. They don't just answer questions. Right. So they can draft, route, research, navigate apps and even compile multi step tasks across different systems. And here's this state of AI agents and in the enterprise right now, Gartner Studies show that 80% of Fortune 500s have active agents right now. Sorry, that was from Microsoft. But gartner said that 40% of enterprise apps are projected to include them by 2026. So yeah, even since that story or, sorry, that report came out from Gartner last summer, I think that has changed. Right. That's one of the things that's difficult to give, you know, people a, a, a thumbprint on kind of the state of AI agents is because even the studies that are the most up to date are usually looking in the rear view mirror a couple of quarters or more. Right. But the reality is a overwhelming majority of Fortune 500 companies have agents in production. All right. And all the software that you use anyways. Right. Because you might think, oh, you know, an agent means building something on your own. No it doesn't. Right. And it's not just the big four in Microsoft, Google, OpenAI, OpenAI and Anthropic. Right. That would give you kind of agents out there in the workforce. There's literally thousands of different now pieces of software. And that's where this, you know, Gartner stack comes from, that 40% of enterprise apps are projected to include AI agents this year. Right. What that means. Good example I've talked about, I've used, you know, ClickUp, a project management tool in the past. Right. So now like ClickUp has, you know, agents by default, I think, you know, things that used to be considered just features or tools in daily software are now agents, right? So if you're using, you know, Salesforce, they have agents. If you're using, you know, Slack Slackbot, you know, they have agents. If you're using HubSpot, they have agents, right? So it's not just, you know, building an agent from scratch or, you know, using one of the tools for the Big fool or these, you know, super general agents, like, you know, Meta's Manus agent. That's weird to say now, right? Manus that was acquired by Meta or Gen Spark or some of these, you know, general agents, right? They're everywhere. AI moves too fast to follow, but you're expected to keep up. Otherwise your career or company might lag behind while AI native competitors leap ahead. But you don't have 10 hours a day to understand it all. That's what I do for you. But after 700 plus episodes of everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one in episode 691. Number two, tap the link in your show notes at any time for the Start Here series. Or you can just go to starthereseries.com which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. So what is it actually? Right, so I'm going to break it down in the most simplest, hopefully language possible. An AI agent is an autonomous system that can plan, execute and self correct across different tools and systems. So it's kind of like a human at a computer, right? So that can make decisions and has access to things. That's the way I like to think of an agent, right. It's the same thing as if a, you know, an entry level worker sat down at a computer at your company that had access to everything, Right? So think of that. But you can have million of them. So what feelings do you get? Is that excitement or are you thinking of security and risk and things that can go wrong? Well, I think when you think about agents and their implications, you have to think of both sides of that coin. But that's essentially what an agent is Right. A full agent is like sitting a human down at a computer that has access to everything, right? It can go get you new clients and close deals. It can complete work autonomously, spreadsheets, PowerPoints, right? Jumping between different apps. It, you know, has access to a terminal, you know, a sandbox, a virtual browser, all these different things like a human sitting down in front of a computer does. So essentially, if it can't take action, if it can't adapt to context, if it can't solve new problems independently, it's not technically an agent. And I'm going to go over some of the, you know, other things that it might be and that's because I've talked about this on the show before. A great, a great study from Gartner talked about this concept of agent washing, right? So they looked at thousands of different vendors in 2025 were essentially marketing or offering or selling agentic capabilities. And what they found, only 130 of thousands were actually agents. It's agent washing, right? Most are just using, you know, chatbots or, you know, scripted rigid workflows and sprinkling some, you know, quote unquote agentic model somewhere in the process and saying it's an agent. I tell you this, even looking at my own inbox, right, for everyday AI, because we get pitched more than a thousand times a year by companies and you know, obviously recently it's just been all agents, right? Oh, check out. Our agent is the best one in the world, right? And most of the time I don't look because it's, I don't think most of the companies are going to make it very long. But then when I do look, I'm like, this isn't an agent, right? This is a, you know, like a, like a workflow. This is RPA with a little bit of a large language model sprinkled on top. That's not an agent, right? An agent. You don't give it a, a dedicated workflow. You don't give it, you know, if then or if else like, like statements to, to follow. An agent is sitting a computer, like sitting a human down in front of a computer. They have free will, make decisions, make good ones, make bad ones. It really depends on, well, the guardrails that you set up around those agents. And right now 88% of executives are investing in Agentic AI, but most can't even tell what's real from fake. So let me quickly talk about the difference, right? Because I said, chances are even if you're paying for an AI agent product, it's just marketing. It's smoke and mirrors because like I said, according to that Gartner study, less than 5% are actually AI agents. So let's first talk about AI powered workflows. Right? And there's nothing wrong with these other things, FYI. Right. But a great example of that is like N8N, right, or, or make.com or you know, in some instances like zapier as well. Right. I love, I, I love these tools, I love these platforms, right. I'm not saying that, that they're bad, right? But if you have all these decision trees and you're connecting these nodes, it's not an agent, right? It's an AI powered workflow. Nothing wrong with it. All right, we're just getting definitions here. All right. Then you have agentic models. Well, what the heck is that? Well, these agentic models are essentially turning into mini agents and they're technically, I think, in my opinion, more agentic than AI powered workflows because they can make more free will decisions on their own and also in the same time do more harm. Right, but agentic model, well that's just our, you know, frontier AI models, right? Think of the big four models. Well, technically the big three that are at least creating and you know, putting their models out there. You could throw in, you know, xai's Grok if you want to. I wouldn't necessarily, but let's look at three models. Google Gemini 3 Pro, Claude Opus 4.6 and OpenAI's GPT 5.2 Pro. Right? Or 53 Codex, whatever you want. Um, and obviously if you're listening to this, you know, at some point later in the year it, you know, just replace it with, you know, today's three models. These models by default are agentic and they didn't used to be, right. A year ish ago, models were not agentic. They were extremely smart. Autocomplete token munchers. Right? Tokens here, spit it out right now. They're not like that. Models are agentic by default. That means you can give them a lot of context, you can give them files, you can give them problems. You can even in the course of a chatbot, right, A front end chatbot, you can technically, if you know what you're doing and give it enough context and you know, go back and forth with it. It can technically have a very agent esque workflow. Right? It's a little more confined because you're working within the constraints of a certain, you know, AI chatbot like ChatGPT. But I'm talking front end, front end. AI models technically are very agentic because they can decide on their own how they're going to solve a problem. They have tools that you probably don't even know that they have that they will call on their own. Right. They have virtual browsers, you know, they have kind of their own sandboxes where they can write and run code. Right. Crazy thing I was doing recently, you know, using codecs. Right, Very good. But I mean technically, I think OpenAI is investing more and more into Codex. You know, it's technically a coding model, but it's a front end model. Right. You don't have to do anything on the back end. I was using it and Codex on its own decided it was going to download a 6 gigabyte open source model and it used it in its own platform. That is crazy. Right? It noticed that it didn't have the capabilities to do something at scale that I asked it to. And then over the course, overnight it worked for almost 10 hours. It downloaded an open source model. And then essentially I had this one was, had like 3,000 screenshots and it wasn't able to, you know, get the results it needed to at scale, at speed with its own computer vision model. So it said, all right, I'm going to download something and a six gigabyte model. And it ran it through there. Right, that's an agentic model. It made decisions, it called tools, it found solutions. Right. Then you have passive agents. All right, sorry, I went on a little side tangent there. So number one, AI powered workflows, number two, agentic models, and then number three, passive agents. So these are agents that are not necessarily autonomous. Right. But same thing. An example of that, right. ChatGPT has a great agent mode. I can schedule that agent to run at 9am, right. But I still have to trigger it. I still have to go say, hey, agent. Right. And I can train it and give it, you know, it's its role and it's, it's context and all this stuff. But I still have to go push the button. All right, and then you have autonomous agents or always on agents. Right. And then these are taking, especially recently, a lot of new shape and form. But I mean an always on agent or an autonomous agent is essentially that it's working around the clock. Right. And it's not just triggered by you. It can in theory be triggered by a certain action. Like oh, when you receive an email, right. It's going to automatically do A, B and C and then go after a certain goal. Right. So even the the, the lines, you know, kind of going over these four different classifications, they're starting to blur as well in the risks. Just like the capabilities are growing just as fast. Right. Some, some good stats here. So this is from Cyber Security. Insiders said that 75% of CISOs have discovered unsanctioned AI tools already running in their environments. Right. So that's, that's crazy, right? When we talk about shadow, shadow AI, it's no longer just oh, someone's using, you know, chat GPT maybe when they shouldn't. No. What about if someone's building agents that have access to all of your data? It's happening. And then those agents are making decisions potentially without any or you know, company approved guardrails. And then when you talk about multi agent systems, that's when there's huge new risks. Right. That you, it's, it's really hard to project because as agents capabilities are changing, humans necessarily, even the humans building them don't fully understand the risk profile of these multi agent systems. Right. So very simple example, right? I didn't know that Codex was going to download an open source model. So what happens right when we set up guardrails, you know, on maybe a certain multi agent system and then the agent gets creative, it finds loopholes or gray area, right? And it says, hey, well it said I can't set up, you know, my guardrails say that I can't use other agents, but it doesn't say I can't use, you know, AI powered workflows. So then it finds a loophole and now all of a sudden you have these agents that are setting up something that is, you know, unregulated agents will do that. Agents by default are made to be quote unquote, helpful assistants, just like large language models. So if they think that it's helpful, they're not going to, you know, go through a, a morality check first or a company ethics check unless you really have that hard baked in there. They're going to go off and make trouble. Probably they're going to break things while getting something done. You know, sometimes I think of agents. It's, it's, it's like giving a, giving a toddler a task, right? Hey, hey child, hey, three year old, you know, go, go put this toy away. All right. Well, did they put the toy away? Sure. Did they knock over a lamp maybe? Did they smear marker on the new couch? Potentially. Right. It's the same thing, you know. And right now Another report, the 2026 CISO AI Risk Report said that 92% of organizations right now lack full visibility into their AI identities. And again, as those AI identities and capabilities are growing, the risk is going crazy. So how do we get here? Right, let's take a very quick walk. So 2022, you know, AI was, you know, hey, type of question, get an answer. And then I think we had two years, you know, in 2023 and 2024, where, you know, large language models, oh, they can use tools now and you can upload files and they can, you know, browse the web and that's cool and it's more helpful. But then I think toward the end of 2025 and obviously this year now this is where these reasoning models are agentic by default and their capabilities are growing by the day. And like I said, the flip side, the unknown risks are also growing. But now reasoning models and reinforcement learning just let agents actually act, right? And even if we talk about things like OpenClaw, right, which OpenAI acquired, you know, these open source agents, people are just giving them computers. They're handing over, you know, their complete lives, right? Hopefully, maybe those are more entrepreneurs, business owners, solopreneurs, right? But people are still doing this. They're giving AI agents access to everything. And we don't yet know their capabilities. But it's quickly gone from AI is just a chatbot that you talk with to oh, now all of a sudden that chatbot has access to tools and the Internet. And oh, now all of a sudden, you know, these agents are making decisions completely on their own and their capabilities are quick. So here's six different agent types that I think you'll encounter in 2026. You know, not every single agent out there, like I said, there's literally tens of thousands technically agents, because you can spin one up in a couple of minutes, right? But you have task agents. These are agents that draft, summarize, create assets, et cetera. You have decision supported agents. These are ones that compare options, surface trade offs and flag risks for review. Then you have process agents. These are ones that route, work across tools, gather inputs and prep the next step. You have computer use agents. These can navigate websites and apps to complete multi step tasks. You have multi agent systems, right? When agents can spin up sub agents, you know, set a bunch of agents out on their own, right? Claude code, you know, from Anthropic recently with their Opus 4.6 kind of, you know, have popularized that on the consumer and the mainstream. Then you also have commerce agents, right, where agents can talk to each other. Right. You know, agents are now having Their own version of the web. Right. There's going to be agentic transactions that really don't involve humans at all. So even the type of agents changing quickly. Right, Talk to me next week. There's probably going to be new categories. So when do you actually need one? When you need an agent vs when should I go do this task myself? Vs when do I need an AI powered workflow? Vs when should I just use chat, GPT or Gemini or Claude or copilot vs a full autonomous agent? Well, I think if you can write a clear checklist and it rarely changes, a workflow can handle it fine. Right. AI powered workflow. If the steps vary, if it requires judgment, or if it depends on multiple tools, that's probably when you're starting to get into either agent territory or an agentic model that has access to your data. But here's the thing. If the underlying process is broken before the AI touches it, an agent is not going to be the answer. I think a lot of people. Well, by a lot of people, I say the overwhelming majority of people are just looking at an AI agent as a shortcut to do things that humans couldn't or to do things that they just don't want to do. It's not going to work like that. If you stick an agent on a broken or an antiquated workflow, you're just asking for a compounding disaster. Right? If you're like, oh, well, our humans aren't doing a good job at this, let's give it to an agent. Well, the agent is going to do a worse job than the humans that weren't doing a good job. So you should really only being applying an AI agent to processes that are already running very smoothly, that are well documented. Right? You have SOPs, you have way to measure, you have ways to measure them. Otherwise, setting an agent out is just a recipe for disaster. And that's why another recent Gardner study said that 40% of agentic AI projects are expected to fail or be canceled by 2027. Well, that's one of the big reasons is what I just told you. People are thinking of AI agents as duct tapes instead of, you know, being multipliers through processes that are already AI enabled and working with humans. Right. Poor data quality, just like anything else. Missing context, no evaluation loops. Because I think success requires rethinking and redesigning processes, not just adding AI on top. You're probably tired of me saying, you know, you don't just upskill reskill, right? The same thing with agents. You don't Just upskill a workflow with an agent. You don't just upgrade a workflow with an agent, you have to deconstruct it, rebuild, you have to unlearn and you build from scratch and agentic first workflow. So we'll talk about bounded autonomy and that's, well, it's how you start losing control if you're not doing this. So you should be starting when it comes to agents. Like I said, pick the right workflow that's already working AI enabled with humans. It's documented, it's processed, it's processing correctly, you can measure it, etc. So you should start at suggest only with an agent where the agent just drafts and you decide what happens next. Once it's gone through that process successfully, then you move on to execute with approval where the agent acts after you one click sign off. Then again then, and this is a big asterisk, right? Everyone's got to sign off. That's when you can start moving on to more autonomy, right? When you can scale if the guard rails are right, you know, with spending caps, permission rules and audit trails. All right, but only then, right, can you think of AI agents as this, you know, infinitely, infinitely scalable asset for your company. So here's how you get started this week. Number one, you need to think about meetings to action items. Have an agent go draft owners and follow ups and you approve before sending inbox triage, right? Another great thing for AI agents, don't have them act right. Have it triage your inbox or multiple pieces of software that you already use or creating research briefs. An agent can build a briefing doc with sources and let you verify them before sharing. So start very simple. Don't let an agent do something that right now requires multiple humans and multiple approval processes. Start looking at maybe some of those time consuming, difficult tasks that an individual human does. And then don't give the AI agent full autonomy. Go through steps. So here is the big takeaway as we wrap up. Agents offer delegation not just answers. They plan, act and report back. They can use tools at their own disposal, come up with new solutions that you didn't even think of. So that's why guardrails, traceability and observability are paramount. Then you need to start with bounded autonomy and measure time saved error rates, risk events, you know, roi. You need to constantly doing those things. You need to don't do human in the loop, right? It's one of the biggest mistakes I think companies are still making because human in the loop was this fun term in 2024 and everyone just said it. No human in the loop is combustible in a bad way, right? People think, oh, we're going to have a hundred agents and we're going to have, you know, Bill and IT oversee them. No, you need expert driven loops with cyclical improvement chains that are chains that are documented. Right? Observability, traceability is a full time job for a team. Go back and that's why I said go back and listen to the 2026 AI prediction and roadmap series. I've talked about this in a lot more depth and told you how to get it done. And then last but not least, the future advantage comes from building agent ecosystems, not just picking the right agent or, you know, dipping your toe in the AI agent of the week. You have to be intentional about it. And investments now, even sandboxing them, not in production investments now in proper AI agent usage are going to be key for when the technology improves. I know it's a tired old cliche, but AI agents are the worst, are worse today than they'll ever be. They're only going to get better. All right, I hope this was helpful. A beginners look at agents. So whether you are brand new to AI and you're getting all excited about all this, all these shiny capabilities, or if you've been building AI agents for a decade, I hope that this episode and also the Start Here series has been helpful. And if it is, please, please please go to Start Here series. Com that is going to give you insider access and free access to our inner circle community and it's going to spit you right out once you join in the Start Here series space there so you can go watch, listen, read more about all of the Start Here series in order. Connect with other people who are doing the same. All right, I hope this was helpful. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.
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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 your everydayai.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.