AI Lens
AI news, hot topics, advancements, and discussions about how AI is reshaping business and society.
Your focused view on the emerging hot topics in the Age of A.I.
AI Lens
Episode 3: 2026: The Year of AI Agents
AI Lens: Your focused view on the emerging hot topics in the Age of AI. We cover AI news, hot topics, advancements and discussions about how AI is reshaping business and society.
Today’s episode is a big one—because 2026 is shaping up to be the defining year for AI agents. Not chatbots, not copilots… but fully agentic systems that can take action, make decisions, and run workflows end‑to‑end.
Industry analysts have been calling 2025 “the year of the AI agent,” but the truth is that most companies were still stuck in pilot mode. Now, in 2026, the shift is real: AI agents are moving from demos to dependable business tools embedded in daily operationsUnite.AI.
So today, we’re diving deep into:
- What makes an AI agent agentic vs. a traditional chatbot
- Real‑world examples of AI agents in action
- Why 2026 is the year they go mainstream
- Business benefits
- Challenges and risks
- No‑code agent builders you can use today
- And the big question: Will AI agents be standard coworkers by 2027?
Welcome back to AI Lands, your focused view on the emerging hot topics in the age of AI. We provide AI news, hot topics, advancements, and discussions about how AI is reshaping business and society. Today's episode is a big one because 2026 is predicted to be the defining year for AI agents, not chatbots, not co-pilots, but fully agenic systems that can take action, make decisions, and run workflows end to end. Industry analysts have been calling 2025 the year of AI agents, but really 2025 was developing the infrastructure for agents. The truth is that most companies were still stuck in pilot mode in 2025. Now, in 2026, the shift is real, and AI agents are moving from demos to dependable business tools embedded in daily operations. So today we'll be deep diving into John. You want to help me out here? What are we going to be talking about?
SPEAKER_00:What makes uh an AI agent agentic versus say a traditional chatbot? Real-world examples of AI agents in action. Uh, why 2026 is the year they go mainstream? The business benefits, always an important one. Challenges and risks. I think that's an often overlooked uh aspect of this. Uh no code agent builders that you can use today. And the big question will AI agents be standard co-workers by say 2027.
SPEAKER_01:So let's get into it. Let's start off with one of John's favorite topics. What makes an AI agent a genic? So, John, why don't you tell us what it how what is a genetic?
SPEAKER_00:Well, I think most people still think of AI as say a chatbot, something that answers questions for you, generates text, helps people brainstorm. But really, AI agents are a different species entirely. You know, whereas a traditional chatbot will respond to your prompt. It doesn't really take initiative, it doesn't operate tools or systems, it doesn't pursue goals or outcomes, and it's much more reactive, not say proactive, while AI agents, on the other hand.
SPEAKER_01:Let me back up for a second. So it's more like automation versus taking it to another step.
SPEAKER_00:Right. So you're automating processes or workflows in your business as opposed to, you know, just um having something give you the reactive answer. However, creative and interesting it might sound, it's just it's just taking it to a whole nother level.
SPEAKER_01:Okay. So give me some examples of what AI bots can do, these AI agents.
SPEAKER_00:AI agents, yes. So they represent really the next generational leap forward for AI. It's moving it from conversation to action, I think it would be a good way of putting it. You know, they can break down goals or outcomes into tasks, use tools, APIs, and external systems to execute. They can browse the web, for instance, analyze documents, trigger workflows, um, make decisions with minimal human oversight, operate continuously toward, like, say, long-term objectives. You know, I think another way of looking at it, it can carry out, like, say, very complex multi-step tasks. It can integrate and interact with third-party systems working toward, like, say, a long-term outcome with minimum human interaction.
SPEAKER_01:Okay. So the difference between them is you can ask a chat bot to write an email, but if you're asking an agent, you might ask them instead: manage my inbox, prioritize my messages, draft replies, schedule meetings, and escalate what's urgent. Give me my priority list. Um, and and you know, we can kind of see that because, for example, if you go into Chat GPT, co-pilot, perplexity, and you put in a prompt, not only will it answer, but it will suggest next steps you might want it to take. So it's not just automating a response, it's thinking and then giving suggestions.
SPEAKER_00:I take it a step further. You know, one is a helper. Well, this is more of like, say, a coworker. We'll we'll dive into that later more. Okay.
SPEAKER_01:So let's give some real-world examples of AI agents to do. And what, you know, what do they do in the various fields today?
SPEAKER_00:So I think in 2025 you saw a lot of piloting going on with AI agents. Uh, but we did see some real-world deployments across some certain industries. So, customer service agents, for instance. Companies are using agents to, you know, resolve tickets end-to-end, pull data from CRM systems, issue refunds, update customer records, and trigger follow-up workflows. So, you know, it becomes almost like a digital assembly line where agents run entire workflows, not just certain tasks.
SPEAKER_01:Right. I think they're also using AI agents now as cybersecurity agents. They're deploying AI agents that can monitor logs, detect anomalies, trigger containment actions, um, and generate incident reports when there's a problem. The shift um it sounds like it's really driven by the fact that as soon as models interact with external systems, the threat surface expands. Some something attacked attackers are already doing. So it can take that information and look for attackers, it can then act, do reports, contact, you know, uh managers, go into and close certain systems down. It can take action.
SPEAKER_00:Absolutely. The nice thing about it is it doesn't get tired, it runs 24-7, you don't have to worry about it, you know, as fatigue sets in, make making mistakes, etc. So it's it's it makes things much more robust and uh and safer.
SPEAKER_01:Okay.
SPEAKER_00:So I think another another area that I've that I would note too is software engineering agents, you know. Again, we're gonna talk about sort of this uh vibe coding thing. Uh, but agents can review code, suggest fixes, run tests, deploy builds, file tickets, you know. So you're gonna see that um the ability of AI agents in the software field expand and take on more complex tasks much more readily than they do today.
SPEAKER_01:Right. And also, you know, the operations and back office agents, um, clerical is really a lot of what it will be taking over. And they're already we're letting agents run procurement workflows, inventory updates, compliance checks, financial reconciliations. These are already things that are being done today.
SPEAKER_00:Yeah, I think that um the finance one is a really good one. I think you see a lot of a lot of uh finance adopt AI rather rather quickly here in 2026. But really, it's it's what we're moving from is testing AI agents to letting them run entire workflows and trigger these real world actions in 2026, if you will.
SPEAKER_01:Right. I know there is a lot going on with AI and accounting, so that that makes sense. Um, all right. So tell me, why do you think 2026 um is the year of the agents?
SPEAKER_00:There's several factors as to why you're gonna see agents that are faster, more efficient, smaller, more capable models. So foundation models have improved dramatically. You see these bench port report benchmark reports where they're doing uh better and better and better over time. And they've made huge leaps in the last few years. And what you're seeing is they're becoming much more faster, much cheaper, with huge context windows and chain of thought reasoning. So that continues to evolve uh exponentially and grow. You know, I think another one is tool use is now much more reliable. Agents can call APIs, they can browse, they can orchestrate tools and do all of this with far fewer errors.
SPEAKER_01:Yeah. And you know what? There are fewer hallucinations. Um, they're still there. So, you know, we do have to double and triple check. But even in the legal world, I'm seeing it uh less and less where they're they're more right on point with the statutes and the cases and and doing things like that. Um, and I noticed it also when we're using Chat GPT, co-pilot, perplexity, grok, all of those, that there are less hallucinations. But caveat, still double check, just like you would a coworker or an assistant, you still double check.
SPEAKER_00:And we'll talk more about some risks uh that that still exist that we need to manage as well. But you know, another thing too, Liz, is I think we've seen a lot of businesses are moving from sort of that curious, let's tinker with it phase, to now they're really ready to start experimenting, especially now with agents. Um, you see at the enterprise level and it's drifting down into small the mid-sized businesses, where 62% of companies were experimenting with AI agents in 2025, but only 23% had scaled beyond pilots. This year you're gonna see those pilots turning into production systems. As again, people become more comfortable with it, they get over the fear, they better understand how to utilize it, and the systems become much more efficient and reliable and simpler to use.
SPEAKER_01:Right. I think a big problem, I know when I was uh talking with business owners in 2025, a lot of the agents that were available still required coding. And they didn't know how to code and they didn't have the time or the money to hire somebody to do, you know, be a computer engineer and get in there and to code for these um AI agents. Now, with all of the expansion and the infrastructure, that barrier is going away. So it it should be a lot easier for people. And I hope a lot of them didn't get discouraged. Um, because go ahead.
SPEAKER_00:Go ahead. Well, I think just the opposite. They're not have they not gotten discouraged, I think they're you see more and more people delve into AI. And that brings up, I think, another factor in that this business pressure is real, right? As other businesses adopt AI and they gain competitive advantages, you can't sit by the wayside and expect to be able to compete as well as you can today in the future. So you need to, you know, you need automation that goes beyond just simple chat. You need autonomous workflows in order to stay competitive and to stay relevant on a go forward basis.
SPEAKER_01:Yeah, it's kind of like uh in today's world, if if you've ever dealt with people who don't use email and they want to mail everything or facts, and you're like, wait a minute, there's email. Um, it's getting there where these where using AI is going to be like that difference between snail mail and email. Okay.
SPEAKER_00:Absolutely.
SPEAKER_01:So let's discuss some some of the business benefits of AI agents.
SPEAKER_00:Well, I think you're gonna see massive productivity gains. Um, that's a no-brainer, right? Agents can run 24-7, they can handle repetitive tasks, they don't get bored, they don't get tired, you know. Um, it's gonna free humans. Certainly not, or sick days, right? Um, it's gonna free humans, I think, for more creative and strategic work. So it actually is gonna make businesses much more efficient and productive and therefore much more profitable. And then I think it's also huge cost reduction, right? Absolutely. I think replacing manual workflows with eugenic automation is gonna reduce operational overhead tremendously and improve that bottom line.
SPEAKER_01:And I would think that with all the information that it can put at your fingertips, you can have faster decision making in business.
SPEAKER_00:Yeah, I think in general, business is gonna move much faster because of it. Because you know, agents can analyze data, run scenarios. In fact, they can analyze huge amounts of data, run multiple scenarios, and take action instantly.
SPEAKER_01:Okay. And then, you know, the customer experience, how do you expect that to change in the business world with AI agents?
SPEAKER_00:Well, it's gonna reduce, I think, wait times for customers. It's gonna make much more personalized recommendations or interactions. It's gonna be able to sense what your customer is, what's a what's gonna be effective in dealing with that customer? It'll sense the emotion of the customer, it'll adjust its tone instantly. That's gonna resolve these issues from end to end.
SPEAKER_01:And I think you know, a good example of that are the customer service telephone AI bots. How many of us are sick of going through the automated system to wait on hold for an hour, then to get disconnected? Um chat the these AI bots can go in there and answer calls, and they can you can have thousands of them, so you won't be on hold. Um, this is like a huge improvement for the customer experience.
SPEAKER_00:You know, one thing too, Liz, that comes to mind is scalability, right? So agents don't get tired, like we talked about. They don't need onboarding, they can scale instantly across departments. So it used to be that you'd have to go through the hiring process, train, read through those that that weren't gonna cut it, bring out more people, go through this long process. AI agents, on the other hand, instantly thousands can be deployed within a day.
SPEAKER_01:So let's talk about some of the challenges that we're gonna at least initially face using AI agents.
SPEAKER_00:Yeah, you know, it's not all smooth sailing. I think this is an area that a lot of businesses don't pay much attention to at this point, but they need to. So, for instance, one of the challenges that you could run into are security risks. You know, as soon as agents interact with external systems, we talked about earlier that attack surface expands, something attackers are already exploding. So you need to be cognizant of that and manage that.
SPEAKER_01:Okay, and reliability. Agents can still hallucinate or take unintended actions if they're not properly managed and not properly constrained. So that's you need to make sure that they are being watched and that you're watching their reliability.
SPEAKER_00:Yep, and that kind of talks leads to governance. You know, companies need guardrails, they need audit logs, they need human-in-the-loop oversight. You alluded to that earlier, right? So these are all critical factors in making sure that AI works as intended because ultimately your brand's on the line as a business, and things could go wrong, and that could negatively impact your brand. And that's something you want to be able to protect and safeguard on a go-forward basis while still getting all the gains and the benefits that we talked about previously.
SPEAKER_01:Right. And there could be a workforce impact. Agents can reshape the roles, the workflows, the job expectations. So that could impact, at least initially, the employees in the in the business itself.
SPEAKER_00:I think this is something one of the most overlooked aspects of AI, right? We all understand people have fear that their jobs are gonna be replaced by AI, or you know, will AI cause problems? You know, we've got to really manage what the employees' expectations are, get them to buy in and to leverage AI because ultimately that's what's gonna benefit the business. And we'll we'll talk about a little bit more later, but this needs to be like a symbiotic relationship between humans and AI. It's not one or the other.
SPEAKER_01:And so the facts are it it should free up their time to do other tasks.
SPEAKER_00:And and it's freeing up the time from having to do repetitive media media tasks that aren't uh interesting, if you will.
SPEAKER_01:We're gonna say menial.
SPEAKER_00:Menial, thank you. For some reason, I don't know. Don't say clerical, okay? Clerical, right? Repetitive tasks that nobody wants to do, right?
SPEAKER_01:Right. And the integration complexity, that's something connecting agents to legacy systems is still can still be a hurdle. Um, some of these long-term systems aren't made to have agents because they were created decades before. So this is gonna be a process, and you should test it in an area where you know that it's made for an AI agent to work in.
SPEAKER_00:Okay, so we've talked about the risks. We understand that companies need to be cognizant and have an approach to manage those risks, right? Now let's get to some interesting stuff. Let's talk about no code agent builders to watch.
SPEAKER_01:Right. And like I said before, some of the hurdles for businesses to enter this is they were like, I don't want to hire a software or a computer engineer to code all this. Um, let's, yeah, let's get into what agents, what AI agents are out there that we don't need to code.
SPEAKER_00:Well, I think that 2026 has seen an explosion of no code and low-code agent platforms um already. I mean, uh go leading into 2026, I should say. Some leading categories to think of would be these no code agent builders that exist. So platforms let you design agent workflows visually. You can do drag and drop logic on your computer screen, built-in tool integrations exist, automatic orchestration. Some examples, for instance, would be agent builder platforms that you'll see from major cloud providers, uh, workflow oriented AI automation tools, uh, enterprise orchestration platforms that have emerged from 2025 to you know, as part of that boom in the agent in the agent field. So uh you see it integrated in different um softwares. You talk about LexisNexis, for instance, and Westlaw, these in the legal field. These tools are really you know democratizing agent creation. So you have you need absolutely no engineering degree. You can what's what they call vibe code talk it into existence in essence.
SPEAKER_01:Yeah, exactly. So let's give our takeaways for what's going on with AI agents and our predictions or outlook as to what's going on. John, let's let's go ahead and start. What are some of the key takeaways?
SPEAKER_00:Well, I think clearly 2026, and this goes to the whole topic of this podcast, is the year of AI agents when they move from pilots to production. You're gonna see a massive integration of AI, a true value creation AI through agents in 2026. And again, these agents differ from chatbots because they act, they don't just simply respond, right? It's that next level iteration of AI. And real-world deployments are already accelerating across, we talked about certain industries, customer service, cybersecurity, operations, engineering. You're gonna see it in sales and marketing, um, all different clerical, all different types of uh fields and and and industries, right? Right.
SPEAKER_01:But and then you also have tremendous business benefits that are coming with AI agents. You do with the caveat, right now we're we're just getting into this, so you have to pay attention with governance and security challenges. But the benefits, I think, right now outweigh the the problems. I think that if you put in the right guardrails, you will see huge advancements in production.
SPEAKER_00:And and these no-code builders are no code builders are making agent creation accessible to everyone. I mean, again, like we've talked about, you mentioned it, you don't have to have a computer science and engineering background in order to effectively implement and leverage AI agents.
SPEAKER_01:Okay, so let's go into the predictions. What do you think, John? Will AI agents be standard co-workers by 2027?
SPEAKER_00:Absolutely. So, and that's gonna cut across most knowledge-based environments, uh, white collar professional environments. You know, I think that every employee by 2027, in fact, probably in the first half of 2026, is gonna start delving with having like a personal AI agent, not just at work, but maybe even at home, right? But departments are run on ingenic workflows, companies are gonna treat agents, AI agents as digital teammates. Um, I think agent oversight will become a standard manager responsibility. So, kind of talked about earlier. The future of work is not human or AI. I think the human plus AI working side by side, especially with these agents, is where it's gonna start, is what the what it'll be at least in the near future.
SPEAKER_01:Now we are recording this on January 3rd, 2026, right? That's right. Next week is CES, and John and I will be in Las Vegas for CES, and we're gonna check out all of the new stuff that's in there for AI, all of the you know, up-and-coming technology that's out there, all the different models and AI agents. So, what we're predicting today could be totally different next week, it could be a lot more. Um, I'm hoping, and I I think we might see some things that are gonna blow our minds. Um, and I can't wait. So, tune in next week to see, you know, what else we're gonna be telling you about AI um agents and all of the advancements that we saw at CES.
SPEAKER_00:By the way, Liz, it's almost like we talk about dog years. There's now AI years, right? So AI takes condenses centuries into decades and decades into years and years into months, or if not weeks. And so it changes so fast. I mean, just think of where we were a year ago to today. So I can only imagine coming out of CES, we're gonna gain some incredible insights and see some amazing products. So I'm excited about this.
SPEAKER_01:Well, that's a wrap of today's episode of AI Lens, your focused view on the emerging hot topics in the age of AI. We provide AI news, hot topics, advancements, and discussions about how AI is reshaping business and society. If you enjoyed this deep dive into AI agents and the future of work, make sure to follow, subscribe, and share this episode with someone who is curious about where AI is headed next. Until next time, stay curious, stay informed, and keep your lens focused on the future.