EP 613: AI Agents: From automation to super agents. 10 AI Agents you should know in 2025

Topics Covered in This Episode:

1. AI Agents Market Growth and Adoption
2. Differentiating AI Agents vs. Chatbots
3. Real vs. Fake AI Agent Identification
4. Seven Categories of AI Agents Explained
5. Enterprise AI Agent Use Cases & Risks
6. Observability and Traceability in AI Agents
7. Detailed Review: Top 10 AI Agents 2025
8. Pros and Cons of Major AI Agent Platforms
9. AI Agent Implementation Best Practices
10. Five-Day Plan for AI Agent Adoption

Keywords:

AI agents, agentic AI, autonomous agents, super agents, AI automation, AI-powered workflows, generative AI, agent washing, agentic browsers, AI chatbot, AI agent vs model, large language models, enterprise AI adoption, agentic model, self-correcting AI, planning AI, AI agent categories, autonomous software developers, general purpose task agents, enterprise workflow automators, specialized research agents, foundational platforms, agent frameworks, web automation agents, user interface automation, conversational companion agents, observability, traceability, governance, Microsoft Copilot Studio, ChatGPT agent mode, Claude Code, Google Project Mariner, AWS Bedrock agents, Salesforce Agent Force, Zapier agents, Replit Agent 3, GenSpark Super Agent, Manus AI, agent sub architecture, parallelism, cloud runtime for agents, no-code AI agent builder, auditability, cost control for AI agents, scaling agents, AI agent pitfalls, agent deployment, agent privacy, agent access permissions, agent orchestration, agent integration, automatable problem, agent ROI, agent evaluation, agent MVP, agent success metrics.

Podcast Transcript

Jordan Wilson [00:00:17]:
There's literally hundreds of options out there when it comes to AI agent options. It's overwhelming. Someone that covers generative AI every single day, I almost like get tired of seeing new AI agents. Part of that is, well, one of the reasons is Gartner came out with some recent research going over the agent washing epidemic, where they found that 95% of companies even promoting AI agents weren't actually AI agents. So what do you do? Do you need to use a bunch of them and find out what's good for you? How do you separate the real from the fake and how do you actually use them? Well, we're gonna be hopefully answering those questions and a lot more today on everyday AI as we go over AI agents from automation to super agents, 10 AI agents you should know. I'm excited for today's conversation. Hope you are too. What's going on y'all? My name is Jordan Wilson, and welcome to Everyday AI.

Jordan Wilson [00:01:15]:
This is your daily livestream podcast and free daily newsletter, helping everyday business leaders like you and me cut through the fluff, understand what's going on, not just learn what's real and what's not, but how we can all actually leverage that to grow our companies and our careers. If that's like, yo, that's what I'm trying to do. You're in the right place. Starts here with the unedited, unscripted livestream podcast. But if you wanna take it to the next level, you gotta go to our website, youreverydayai.com. There, go sign up for the free daily newsletter. We're gonna be recapping all the highlights from today's show in case you missed anything and while you're out walking your dog or wishing I would shut up, so you could finish your walk on the treadmill. Right? But we're also gonna keep you up to date with everything else that's happening in the world of AI news.

Jordan Wilson [00:01:59]:
There's a lot, over the past twenty four hours, so make sure you go check that out in the newsletter. So AI agents, what's real? What's not? What should you be paying attention to? It seems like almost an endless onslaught. Like agents are being shoved down our throats, but what's the difference between an agent in chat g p t? Should we be using agents? How and for what purposes? That's what we're gonna be talking about on today's show. So we're gonna be going over and diving into the explosive growth of the agentic AI market. We're gonna uncover the hidden challenges and potential pitfalls in agentic AI adoption, tell you the real reasons you should be paying attention to AI agents in 2025, not just because they're shiny and new, but the real reasons. And we're gonna break down the 10 most popular and widely used AI agents across different disciplines. Y'all wanted this. Alright.

Jordan Wilson [00:02:52]:
So, yeah, if you listen to the podcast, when I say go sign up for the newsletter, it's for reasons like this. I work for you guys. Right? In the newsletter, I said, hey. Are we talking about agents too much or not enough? And you guys overwhelmingly said, feed us more agents content. So if you're a a listener of the podcast and you haven't subscribed to the newsletter, that's the only way I can ask you guys what you wanna know more of, but you said you wanted more agents. And at the end, I've been doing this. I've been putting together these little kind of bonus guides because I can't fit everything into, you know, thirty, forty minute episode. This one's this one's good.

Jordan Wilson [00:03:28]:
Alright? I've I'm not I'm not gonna brag. I've pulled out some banger, like, extra bonus content this week. So if you want if you want access to it, there's 20 total agents. There's 10 that were really good, but I couldn't just make the cut. So go repost this show on LinkedIn. The link is always in the podcast show notes. We put the link in the newsletter, and so make sure you go repost that, and I will send it to you. It is done.

Jordan Wilson [00:03:54]:
It is ready to go. It is a comprehensive guide on 20 AI agents. So let's talk. What the heck is an AI agent? Well, an AI agent can plan, act, and also self correct. That's the biggest thing, to get work done. So an AI chatbot, you're just chatting with it. An AI agent can plan on its own. It can reiterate, and, ultimately, it takes actions on your behalf.

Jordan Wilson [00:04:19]:
The whole human in the loop thing that I hate, think human in the loop is bad. I think expertise driven loops are what we should be focusing on. Anyways, you gotta have humans overseeing AI agents because they can make decisions and actions on your behalf, which is both really cool and extremely useful for enterprises, but also extremely dangerous. Because if you don't have the right humans driving that loop, right, the feedback self correcting loop, yeah, agents can be bad. Right? And I've talked about that plenty on the show. But the AI agent or sorry. The, AI agent market is projected to explode past $7,500,000,000 this year. I would expect it goes well beyond that.

Jordan Wilson [00:05:04]:
And this represents, I think, a pivotal shift from AI. Right? That just answers our questions to get things done. Right? And we've seen whether they're advanced or simple offerings. Right? Because, you know, at this time last year, there weren't really no AI agents available from the big players to the general public. Now there is Microsoft, Copilot Studio, ChatGPT, their agent mode, Claude, Anthropics Claude, has multiple different agents, a lot of them for coding, but they have a computer using agent as well. Google, they have their project mariner, project astra. Hopefully, they release their agent space to the masses soon. Right? But every single big player has an agent that's available today.

Jordan Wilson [00:05:58]:
Are they all great? No. Are some of them really, really good? Absolutely. But whether you want to know it or not, the future of work in AI native, workplaces isn't talking to a chatbot. It isn't just like, alright. Well well, now we have a chatbot plus our business, you know, info. We have rag. We have, you you know, these connectors. No.

Jordan Wilson [00:06:23]:
It's smart humans like you and me overseeing agents. Right? And right now, studies show that over 80% of enterprises are adopting agents right now. So there's massive opportunities for all of us tuning in because because if you're tuning in and listening to this, you're ahead of the curve. But it's all about what are you gonna do with this information, and you have to be able to understand the risks. So, like I said, what does how is this getting to the point now? Because it's more than just talk. Like I said, we've been talking about AI agents on this show for multiple years, but the good thing is I got the receipts two years ago. I'm like, nope. We're not ready.

Jordan Wilson [00:07:04]:
2024, too early. Now we're ready because agents are already showing up where we work. That's the thing. Maybe eighteen months ago, two years ago, you had to really go out of your way and try really hard to make anything on the AI agent side work. You had to have a lot of duct tape, hope, and dreams, and maybe you got a little juice out of it. Now agents are showing up in the actual enterprise systems where we work. So that's in our documents, emails, CRN, browsers, IDEs, etcetera. And the tooling has obviously matured because the models, right, most AI agents are powered by one or a series of underlying models.

Jordan Wilson [00:07:44]:
Right? A lot of them have five, ten, plus models running, kind of the agentic capabilities, and a main agent will break off a complex multi step task to multiple sub agents, and all of those sub agents are running. You know, maybe one of them is running a coding model, from Anthropic. Maybe one is running, a reasoning model from Google. Maybe another one's running, you know, like, you know, GPT 4.1, from OpenAI or something like that. So these models themselves are much more capable, much more robust than they were six to twelve months ago, especially now that we have these reasoning models and, you know, all of the scaffolding that comes along with these models. And that's another reason why they are not just explosively growing right now, but they're actually explosively useful if you know what you're doing. Right? The tooling has matured and big vendors have shipped platform agents as well. Focused startups have shipped specialized agents.

Jordan Wilson [00:08:41]:
Everyone is shipping them, and they are now more than just copilots. They are pilots. They can go out and do work on your own. Right? Very simple use case. I have chat g p t, their agent mode, probably one of the easiest to use, not the most useful unless you are decent enough at prompt engineering. But once you get it, I have my agent. It's on a scheduled run. I don't do anything.

Jordan Wilson [00:09:06]:
It just goes out, execute task for me every single day at a certain time. So, again, what's the difference? What's the difference between an AI agent versus a model? Alright. And, hey, live stream audience. Love love to see you tuning in. Sorry. I should give you a shout out a little more. But let me know if you have any questions. Someone said I would love to see an episode on perplexity's agentic browser comment.

Jordan Wilson [00:09:32]:
Already did it, LinkedIn user. So go go check that out on our website. It's free. Go go, go go check it out. But, yeah, livestream audience, if if you have any questions, please please let me know. I'll try to answer them. Just put a question, in your in your comments so I can make sure, I can see it just because we have comments rolling in from all the different platforms. But what's the difference between an AI agent and a large language model? I've done an entire episode on this, but to put it simply, the lines are blurry because now the base models are agentic in nature.

Jordan Wilson [00:10:06]:
Whereas nine months ago, they weren't. Right? So, essentially, we had our, let's say, rewind the clock a year ago. The models, the most powerful models in the world were transformer models. They didn't really have, a bunch of useful tools. Right? All the scaffolding that make models really good. And they couldn't they weren't reasoning models, so they couldn't plan and think and retrace their steps and come to a fork on the road and try a couple of things on the left side of the road and figure out, oh, that's wrong. I need to go back to the middle and use some different tools. Right? That's an, agentic model with tool use.

Jordan Wilson [00:10:42]:
We didn't have that. Right? So the lines between what is an AI agent and what is an agentic model, they are kind of blurring. But for the most part, if you think of traditional old school, AI models, large language models, they're chatbots. Right? They're obviously becoming much more robust and they're becoming entire operating systems now, but, you know, you are still having as a human to take that information and do something with it. Whereas now, AI agents, you give them a goal, you make sure they have access to the data that they need, and then they go figure it out. Right? Whereas I'd say more, chatbots kinda self-service. Right? AI agents, autonomous cars, they do it for you. An agent holds a goal, chooses its own tools, takes its own action, and checks its own work.

Jordan Wilson [00:11:32]:
And a lot of times, it's not just taking the best available road. It is really in real time building its own role. Right? So let's talk about categories of AI agents because there's a lot, and this is also where it starts to get a little confusing because like someone already said, you also have your agentic browsers now. Right? Which I actually think at least for hey. What has more utility today? I would say it's almost in some use cases, agentic browsers. Right? If you look at top to bottom, if if if you pick a middle of the road AI agent versus an agentic browser, in the short run, I think age, agentic browsers have much more, utility for the average business leader right now. But we're not talking about, agentic browsers. We're not talking about agentic models.

Jordan Wilson [00:12:24]:
We are actually talking about AI agents, complete systems, and I would break them into these seven categories. So number one would be autonomous software developers such as Devon. Right? So these are agents designed to handle end to end software engineering tasks from writing and debugging code to testing and and deployment. Then you have your general purpose task agents, which is like chat GPT agent mode. So these are platforms that act as a versatile digital assistant that can plan and execute a wide variety of complex multi step task for professionals and consumers. You have your inner, enterprise workflow, automators, that would be kind of the third category. These are like Microsoft Copilot Studio. Right? They can automate company wide workflows, and they're all connected to your dynamic enterprise data.

Jordan Wilson [00:13:10]:
Then we have specialized research and analysis agents such as GenSpark. Alright? In a kind of an AI agent start up. So these are agents that focus more on gathering, synthesizing, and analyzing information from various sources to generate to generate comprehensive reports, summaries, or insights. Alright? Then you have your foundational platforms and frameworks. This is more like, Amazon Web Services bedrock agents. So these are the underlying toolkits, cloud services, and open source libraries that developers use to build, deploy, and manage their own custom AI agents. Then you have kinda your UI and, web automation agents. There's a lot of more of these recently than there were, you know, maybe six months ago.

Jordan Wilson [00:13:55]:
So these are agents like, you know, the UiPath, agentic, offering. So these are kinda like GUI. They work in a GUI way. So they work on the graphical user interface, but they're agents that specialize in interacting with software through its graphical user interface enabling automation of applications that lack modern APIs. So technically, right, this is kind of where agentic browsers would fit in, but I I I do think they're a different category. They're not agents, they're agentic browsers. Then you have your conversational, last but not least, you have your conversational companion agents. This is more for agents who kinda like personal agents.

Jordan Wilson [00:14:32]:
So it's it's almost like a blend between a standard, AI chatbot, but can also go out and do things for you. Right? So this category is for agents whose primary interface is empathetic dialogue, but they still perform simple goal oriented actions like setting reminders or finding information to assist you personally. An example of that would be Inflection AI's pie. Alright. What do all these AI agents have in common? Right? I I broke down kind of seven different categories because they're all very different. Right? A lot of people think an agent, all agents are just general use case agents, and that's not the case. Right? And when you're choosing, hey, what should our company be doing or what should I personally be doing when it comes to, AI agents? You have to think of, well, what's your goal? But one, some core, functionality that most AI agents share, they break big goals into smaller steps. They call on and use different tools, and then they click through or use some, type of user interface and then fix mistakes as they go along.

Jordan Wilson [00:15:42]:
Right? That's the other thing. AI agents are iterative. A lot of them will test and deploy things on their own, go back, which I know it sounds weird, go back and fix the own errors that they created. Right? Just like a human would or a team of humans. They pull live info from your data sources, from connectors, from search, from your company knowledge, sometimes from rag data rag databases. Right? But they also keep humans in the loop for risky steps and leave a clean paper trail. Right? So almost every single AI agent we're gonna be talking about, there is a level of traceability. Right? And that's so important when we talk about AI agents.

Jordan Wilson [00:16:18]:
Right? Because let's be honest, when we think of, oh, human in the loop, I think we think of, you know, kicking our feet up on the beach with a margarita, and and our AI agents are doing everything for us. That's not good. Right? But you always have to keep observability and traceability in mind. That's number one. Can you observe the agent as it works? But traceability is after the fact, can you go back and see step by step what the agent did? Right? So as an example, in Chatt GPT agents mode, you can you can click the three little dots. You can either watch it in real time observability or traceability. After the fact, you can always go, click on kind of the, the the reasoning data. It'll extend it, and then you can literally see step by step a screenshot and a description of what the agent did.

Jordan Wilson [00:17:02]:
That is a very simple example of traceability. So, most AI agents do have those things, but it's not all fun and games. The pitfalls are enormous. Right? I think especially and and and this is maybe an another conversation for another day. I think AI in general, because so many people have become over reliant on it, it's taken our normal human guards way down, which actually makes these hidden challenges and pitfalls way more consequential than they, in theory, should be. Right? Sometimes when you're using an AI agent, what you start to do is right? Kinda like what I'm doing. You're just sipping sipping the coffee, and you're like, oh, yeah. Good job.

Jordan Wilson [00:17:44]:
Good job. This would take me five hours. You didn't five minutes. I'm gonna spend five seconds looking over it. Perfect. You can't do that. You have to be vigilant. Alright.

Jordan Wilson [00:17:53]:
But some of the most common pitfalls is something just looks done, but it isn't actually done, until you go through and go through the step level logs and, you know, past, past failed checks. Permissions and sometimes, in some cases are too wide. AI agents sometimes have too much power and humans aren't doing enough job, to rein that in or control it on the front end. Sometimes there's no per agent identity. There's no accountability, and but you need to be able to add approvals and change control. Also costs. Right? Costs can creep up when agents keep looping forever. Right? So a very simple example of this, if you've ever gone into chat g p t and you have the wrong mode selected, right, and you give it a very simple question and, you know, now they're, you know, as of last night, there's even new, you know, thinking levers, inside chat g p t.

Jordan Wilson [00:18:44]:
Right? There's the light standard heavy and, you know, light standard extended and heavy. Right? So in the same way, a normal large language model that can reason, you might choose the wrong one, and it's thinking for twelve minutes on, like, you know, when was the last time the Chicago Bulls won an NBA championship. Right? The same thing the same kind of mistakes in terms of these endless loops, can happen for AI agents. The difference is depending on how you set them up, you might be paying for usage. Right? So you might accidentally be like, oh my gosh. It's just this this never ending loop, that you're not paying attention to, and then you go look at your your your API bill, and you're like, oh my gosh. This it would have been cheaper to have a human do this. Right? You see that all the time.

Jordan Wilson [00:19:24]:
So you need to have clear SOPs and ownership, and fast rollback as well. Alright. Enough chitchat. Top 10 agents that you need to know. Here we go. OpenAI's chat g p t agent mode. This is more of a generalist virtual computer. Right? Chat g p t agent mode has access to a virtual computer, a sandbox.

Jordan Wilson [00:19:48]:
It can run code like it would on a computer, a browser, etcetera. So what it does, it runs multi step tasks end to end. It can research, draft, analyze, and build files inside of Chat JCPT. How it works? It uses a secure cloud computer with a browser code and adds files, narrates each step and ask before high impact actions. You can also log in, to different websites on a browser. You can open, you know, you can temporarily take control, open up different browsers, log in to different services if you like, and then hand it back over. So at any point, you can take over, which a lot of these AI agents, you can do something similar. The pros and the cons of ChadGPT agent, well, it's one of the easiest to use and it's extremely versatile.

Jordan Wilson [00:20:25]:
It's in one place. Right? 700,000,000, weekly active users to chat GPT have access well, not all of them because you have to be on a paid plan. But, you know, tens of millions of people right now have access to an agent that is the learning curve is essentially nothing. Is it the best? Absolutely not. If I'm being honest, I'd say out of these 10, it's probably only better than maybe two of them. Right? But you have to pay attention to chat GBT's agent mode because it's gonna get way better in the future. Obviously, they have their agents SDK. So, companies are building on top of it, and you should be using it anyways.

Jordan Wilson [00:21:04]:
I think it's one of the easiest ways, and that's why I put it first on the list. It's one of the easiest ways to understand how an agent works. You have to go back, look at the, you know, the observability traceability that I talked about. You're gonna learn so much just how an agent calls its tools, how you should be talking to an AI agent versus an AI chatbot. So much can be learned when you go back on the observability traceability side and, you know, track it. So some of the unique features, it chains multiple tools in one persistent session. So context and output stay together. So, yeah, you can keep the context going inside of one agent chat just like you would a normal conversation inside inside Chativity.

Jordan Wilson [00:21:45]:
This is best for busy professionals who want one agent to go from, from question to finish deliverable. It's not great right now. Let me just say that first and foremost. It's not great, but it is the easiest to use. It is the most widely available. And I think out of most of these, it probably has one of the higher ceilings. Next, probably one of the most used here would be Microsoft Copilot studio agents, and these are more, the category would be kind of a governed enterprise or, orchestration. So what does and this is different than Microsoft Copilot.

Jordan Wilson [00:22:21]:
People get confused. They're like, oh, Copilot Studio. I use that, and I'm like, okay. Show me how. I'm like, no. That's Copilot. You're just chatting with Copilot or you're using Copilot in one of the 62 places that it exists within wind, you know, Windows three sixty five Copilot. Microsoft Copilot Studio is a dedicated autonomous AI agent builder that you can build something with no code.

Jordan Wilson [00:22:41]:
So it lets nontechnical people as well build agents in natural language for Microsoft three sixty five. So it taps into the Microsoft graph, intra identity and DLP. Admins can add approval in guardrails. If they want, they should. And agents live where office users already work. That is the big key. Pros and cons, it has enterprise grade governance and deep app hookups, and it's tied to Microsoft's ecosystem, and it needs setup time. That's the thing.

Jordan Wilson [00:23:12]:
Everything like, one of the biggest downsides to Microsoft three sixty five Copilot in general is it's extremely difficult, to handle permissions, access, things like that. The learning curve is actually, I think, way steeper than it needs to be. I hope Microsoft improves on that in the future. What's unique about this? Well, each agent gets an Azure Entra identity with full audit. So IT orgs, or IT departments can treat agents like real users. They can go track anything an AI agent does across their entire organization as if it was an actual employee because different people within your organization can use the same agent. Right? Sometimes people are collaborating with the same agent at the same time. You know, one agent might be used by just one person.

Jordan Wilson [00:23:55]:
It might be used by an entire team. It might be used by thousands of employees across the organization. So that's a good unique, feature for the, Copilot studio agents, and it's best for organizations, obviously, who are already deeply ingrained inside the Microsoft or Copilot ecosystem. Next, quad code. So this is kind of safety first coding execution agent. So what it does, it plans at its runs and test code changes, turning clear tickets into reviewable patches. So if you're in software engineering, anything on the dev side, you probably use Cloud Code. It has been one of the more popular, agentic coders and one of the first now.

Jordan Wilson [00:24:34]:
I think there's maybe some that might end up being, more useful in the long run, but Cloud Code is one of the most popular and most widely used, coding agents in the world right now. And it's really, really good. As much yeah. I know people, like, realize and I get messages all the time. People are like, oh, Jordan, you always, you know, poo poo on Claude. Claude's great for coding, software engineering. I say it all the time. That's not the majority of our audience.

Jordan Wilson [00:24:57]:
Right? The majority of our audience is nontechnical. So if you're using claude.ai in the front end chatbot, not very useful, not very helpful. If you're a software engineer, developer, Claude Code, absolutely amazing. Right? And, Claude Code also has sub agents where it can, you know, break these tasks up. It can work for, I think they said, up to ninety minutes on its own. So how it works, it modifies files and run tests runs tests and loops, logging each step for easy review. Some of the pros and cons, it's safer, test guided changes and explainability. But on the downside, it needs a lot of testing QA.

Jordan Wilson [00:25:31]:
A lot of times you'll ask for one small change. Right? If you're working across an entire repo, you you know, an entire, app, you ask for one change and it might change absolutely everything. And at least over the last, couple of weeks, Claudia has had some, problems. The service, has not been as good. Everyone's kind of jumping ship over to OpenAI's codex, which did not even make the list. But regardless, Claude Code is one of the best. So some unique things, it's a purpose built coding loop for small and safe edits and clean, diffs and it's separate from the chat interface. Obviously, Claude code is not going into claude dot AI encoding in there.

Jordan Wilson [00:26:09]:
It is a separate product, you know, like I said, with the agent sub agent architecture. And this is best for teams automating refactors, upgrades, and fixes where automatic build and test pipelines are already in place. Alright. Next AI agent you need to know, AWS bedrock agents and agent core. This is more on the governed enterprise integration side. So what is this? Well, it's a cloud runtime. So this is an agent that runs the cloud for building and scaling custom enterprise AI agents. How it works? Agent core adds session isolation memory, ident identity and observability.

Jordan Wilson [00:26:42]:
It's It's kinda like Lego blocks for agents. So obviously, if you're a large enterprise using AWS, a u AWS bedrock agents. Right? Where do you start? You always start where your data lives. So, for I don't know how big of a market share, AWS has in the cloud. Right? They're one of the three big. You could say Oracle now is in the big four of cloud AI, but, you know, you're always like, where do I start? Well, so many I think so many organizations are on AWS, at least maybe on the smaller side and aren't fully investing in bedrock agents, which you probably should. So pros and cons, it's highly module, highly modular and framework agnostic. Another good thing about AWS is they have so many platforms.

Jordan Wilson [00:27:25]:
Yes. They have a strong, partnership obviously with anthropic and their investment there. So using the the clawed models, but you can use just about any different models, proprietary, open source, closed source, etcetera. So what's unique? Well, it's plug and play, to run many different agent patterns securely on AWS, and this is best for builders who want company wide standard agents on top of their current existing AWS infrastructure. Next, another company that sometimes I poo poo on, Salesforce. But if you are a dedicated Salesforce organization, if you're using Salesforce for your CRM, which so many people in the world are, I'll say this. Agent Force, in some cases, if you don't care about cost, it's better than not using it. Right? It's better than probably manually having to go through, all of your, Salesforce information.

Jordan Wilson [00:28:17]:
So what is Agent Force? Well, it automates sales and service steps inside of the, Salesforce CRM that teams already live in. So it uses, the CRM context to plan actions. It has a command center that provides approvals, oversights, and outcome tracking. It can send emails to, you know, prospects that have gone cold based on your data, based on your conversational history. So some of the pros and cons, well, it's trusted, data grounded actions, and it shines most if you're already deep in Salesforce. So like I said, if Salesforce is not, you know, obviously for sales teams that are very dedicated and use Salesforce all day, Salesforce agent force is a no brainer. If you're a company that, you know, let's say you use Microsoft three sixty five Copilot, you're just in Salesforce every so often, Agent Force might not be the one that gives you might not be the agent that gives you the best ROI. So who is this best for? Well, revenue and support teams that want measurable impact inside their system of record.

Jordan Wilson [00:29:14]:
Next, Google Project Mariner. This on the list is probably the least used AI agent. Alright. But Google is with OpenAI. They're running ahead of everyone else. So even though their Project Mariner, not that great. Right? It's a Chrome extension. You have to be on a higher tiered paid plan to use it.

Jordan Wilson [00:29:38]:
You still have to pay attention because eventually, Google will release agent space to the match masses. This is their, kind of premier AI agent platform. However, it's been hardly released to anyone. Right? So I was at, Google Cloud Next when it first started rolling out back in May. You know, here we are five or six months later, and still very few organizations have access to agent space. I know we have a lot of people, you know, listening at Google. So Google, please, start rolling this out to, SMBs and, you know, enterprise companies in general. I think it's a fantastic product, but hardly no one has used it.

Jordan Wilson [00:30:13]:
So I can't really talk about agent space because I haven't even personally used it, but I personally use Project Mariner, so I can talk about that. So this is more of a, lives in the browser. Right? But you like, if you have access to Project Mariner or if you can get access, and you are a leader on your team when it comes to AI, this is one of those things you gotta get the reps in. Right? Because Google and OpenAI are gonna continue to run away with everything. So you need to get used to their agent tools with where it's available now even though most people right? Google's Project Mariner might get rolled up with project Astra and agent space. Who knows if it's gonna be its own dedicated offering in the future, but you should get used to the technology and how it works. Like I said, one of the easiest ways to learn AI agents right now is to go use chat g v t agent mode and go observe its steps. You're gonna get better at prompting AI agents.

Jordan Wilson [00:31:03]:
It's completely different than prompting a large language model. The same thing can be said for using project Mariner. So here's what it does. It clicks through real websites for you at scale without manual scripts. Right? So it's essentially a, browser using agent. It's not a computer using agent where ChatGPT's agent mode has use of essentially a virtual, computer, including, like, things that you can do outside of the web. So Google's Project Mariner is more of just web based. So it runs isolated clouds cloud browser sessions, plan steps, fills forms, and can work in parallel for speed.

Jordan Wilson [00:31:39]:
So pros and cons, it has great, obviously, real web execution, but the packaging and scope are still evolving. So what's unique here? Well, it can run up to 10 concurrent browser sessions to finish web tasks faster, which you don't have that in, like, chat g p t agent mode. You're kind of restricted to one run at a time. So you have this, kind of parallel, advantage here with Google's Project Mariner, and this is best for teams that need repeatable web workflows, done in handoffs reliably. One thing I like about Project Mariner is it has this teach and repeat thing. So if you have a process that you manually do over and over, I should have used this for a project that I was, I was working on yesterday. I was trying to do it inside, both, Perplexity's comment and chat g p t agent mode. They weren't doing the best.

Jordan Wilson [00:32:25]:
I probably should have done it in Project Mariner because it has this teach and repeat pattern. Right? If you have, you know, five different websites that you all use concurrently, you're grabbing something or some information from one, carrying it over to two, making changes, bringing it over to three. That's a great use case for Google's project mariner. But come on, Google. Give us agent space. Next, and this is a more recent one and one at least on the list that I've probably been the most impressed by recently, and that's Replit Agent three. And this is an autonomous software creating agent. So So here's what it does.

Jordan Wilson [00:32:57]:
It turns plain English into working apps, talking full scaffolding, coding, running tests, fixing everything, front end, back end. It is a absolute workhorse. So you'll notice that some platforms didn't make the list. I didn't put cursor on here. I didn't put lovable. I didn't put bolt all great, all great tools, but, I think replit agent three outshines them. Alright. So it also uses a reflection loop to execute tasks, tests in a browser, and auto repair, issues inside of replit's IDE.

Jordan Wilson [00:33:35]:
So some of the pros and cons, super fast idea to app, but complex code still benefits from human review and controls. This is one of those, can a nontechnical person use Replit agent three? Sure. But your outcomes are gonna be much better if you know what the heck is going on. Right? It's kinda like watching a a movie, in a different language you don't speak without subtitles. Right? You're like, okay. I think I know what's going on because I can see everything. And I I think I'm following the story line of of turning plain English into a working app with a database and a back end authentication and payment processing. Right? What Replit agent three can do is extremely impressive, but it probably makes more sense if you speak the language.

Jordan Wilson [00:34:17]:
Right? So, it's almost like some of its most, I would say appealing use cases are for nontechnical people because technical people have plenty of AI IDEs they can go work with. Like I said, Cursor, Lovable, Bolt, you know, Claude Code. I think Replit is is one of those that's probably gonna separate itself, over the next year, and become wildly even more popular than it already is. So what's unique about this? Y'all, three hour run time. An AI agent can go run for three hours, and I've seen some videos on this. Right? Unedited before and after of what it can do in two to three hours. Wildly impressive. So, yes, some of the other platforms, the lovable, the bolts.

Jordan Wilson [00:35:03]:
Right? It can accomplish the same things, but with a lot of back and forth iterative prompting. Right? So if you get it right on the front end and if you know what you're talking about again, think of the movie analogy. If you can direct the movie, replant agent three can go do it three hours. So this is best for founders, educators, and developers who want prototypes live and fast. Next, Zapier agents. I've been very impressed. Zapier agents are really good. So what it does, this is no code agents that automate work across the more than 7,000 enterprise apps that Zapier connects with.

Jordan Wilson [00:35:37]:
So I'm not even gonna get into, like, the difference between, well, I'm I I will shortly, actually. Right? I think a lot of people when they think AI agents, they're thinking AI powered workflows. And, yes, Zapier has that as well, but their agents offering is completely different. So, yes, you can still have your, you know, kind of your visual builder building different, you know, marketing automation workflows and enter in AI because a lot of people think of, you know, like, oh, n a n. That's an agent. I'm like, no. That's an AI powered workflow. So Zapier does have AI powered workflows, but they have their actual agent as well.

Jordan Wilson [00:36:11]:
So completely different product, and it's extremely impressive. So all you do is you describe the outcome you want. The agent selects selects the app actions. It runs them, and then it can hand certain things off to other agents. So some of the pros and cons, well, there's massive app coverage more than 7,000, so you don't even have to worry. There's support for MCP, but you don't even have to worry about creating your own custom servers because Zapier connects to the Internet. It is the glue that holds the Internet together. So that is a huge advantage of Zapier agents.

Jordan Wilson [00:36:40]:
So some of the downsides, the cost and complexity can grow. Right? Especially with more open ended flows. What's unique? Well, there's agent to agent calling, live knowledge sources with drive, box, Dropbox, dashboards, and ready to use templates. And this is best for ops teams, marketing sales, and support teams wanting practical cross app automation without code. Right? So I think Zapier agents, have a place. It's not like you choose one AI agent. I think, Zapier is ultimately, going to continue to rise, in relevance outside of those in the marketing world. Right? I've been using Zapier for more than ten years.

Jordan Wilson [00:37:22]:
I love it. It's traditionally been for marketers. I think nonmarketers are eventually gonna catch on to Zapier because of Zapier agents. Miguel said, would you say Zapier is better than n a n? Yes. Absolutely. N a n is free. It's open source. Zapier's better.

Jordan Wilson [00:37:39]:
Right? Don't get me wrong. N a n is great. A lot of people right. Stop stop seeing it. Right. A lot of people see, like, what's on social media and that, you know, it's all these n a n workflows, you know, comment, follow me, repost this, and do this 30 things, and I'll send you this agent that prints money. It prints a million dollars. Right? It's no.

Jordan Wilson [00:37:57]:
It's just a it's an AI powered workflow. You know? Part of the reason I think n a n gets a bad name because people are abusing it and and selling it as snake oil. So, but no Zapier is better. Alright. Next, we have GenSpark Super Agent, and this is an orchestrated research and creation. So here's what it does. It produces research pages, summary slides, and media fast. The research and creation process in Genspark, extremely impressive.

Jordan Wilson [00:38:23]:
And both Genspark and Manus, to their credit, keep churning out updates a lot faster than, as an example, Copilot Studio or Chad GPT agent mode. They're really shipping fast. So here's how Genspark super agent works. It routes subtasks across multiple models and tools. I think the last time I checked, it uses nine different a, large language models and then it stitches together a clean result. So essentially, you know, you have a routing agent, and then there's specialized sub agents and they all kinda work together. They all go off and do their own thing, report back to the main agent and then give it back to the user. So the pros and cons well as broad and quick.

Jordan Wilson [00:39:01]:
You can verify key outputs before publishing in a professional setting. So what's unique about Genspark? Well, it has this thing called spark pages. Think of them as essentially, like, almost a cross between, like, a website and a PowerPoint. Right? But they are shareable pages, that you can go in and update, and this is where all of this information can live. So instead of just having, you know, a bunch of text or some random, you know, oh, it spits out a PDF. It's like a way for your, content that you create with the Genspark Super Agent to live. And this is best for analysts and marketers who need synthesized deliverables without heavy lift. Alright.

Jordan Wilson [00:39:40]:
Manus AI. And this is more of a hands off autonomous executor. So what does Manus do? So, Genspark and Manus are both, out of China, which is why maybe you've heard of them and maybe you haven't. Right? We, but they are, obviously, I think, starting to creep their way into The US ecosystem as well. So what does Manus AI do? Well, it handles long multi step projects and end then shows you every step it took. So how it works is it works in a cloud desktop and browser environment similar to chat g v t's agent mode, but it plans action and it logs everything and keeps going even if you disconnect. So I will say it is much more robust right now than chat GBT's agent mode. So but they work very much in the same way in terms of the user interface and user experience.

Jordan Wilson [00:40:26]:
So some of the pros and cons, well, there's deep autonomy with transparent trace, but also, there's my privacy and access policies. So what's unique? Well, persistent cloud sessions work, continue even when you go do something else. Right? So that's nice. Chad GPT's agent mode is hit or miss with that. So essentially, you can log in, or keep these instances kind of live and running across multiple days, multiple weeks inside of Manus AI. So this is best for I'd say smaller teams, creative teams, or consultants and researchers who want an agent that just kind of does the work. Right? So I'd I'd say Manus is a great kind of starter agent, for a lot of people. Alright.

Jordan Wilson [00:41:09]:
That's our 10. We're gonna keep this thing going. Couple things to wrap up here. So what's the difference? Well, I gave you the different categories, but let me start breaking these now because each platform offers different runtime primitives including identity, memory, isolation, observability, and parallelism capabilities. I told you, some of these agents don't run-in parallel. Some of them do. Some of them have persistent cloud instances. Some of them don't.

Jordan Wilson [00:41:32]:
So, you know, every single thing like a car. Right? Some have four door, some have tube, some are hybrid, some are electric, some are four seaters, some are are vans. Right? So it depends on what you need and what you want. But the platforms vary in domain grounding depth. You know, some are CRM native, some are IDE native, some are browser native, etcetera. And the delivery approaches range from no code studios like Microsoft Copilot Studio to framework run times to embedded application agents. Right? Some are great at certain tasks right now. And I do think, you know, as we go on, there's, like, marketing specific agents.

Jordan Wilson [00:42:07]:
There's sales specific agents. There's HR specific agents. There's legal AI agents. Right? Harvey. Right? There's there's so many, different agents for different categories as well, but then there's also different genres even within those categories, which is why it is sometimes hard to keep up. But cost structure is also significantly differ. Some of them are subscription models and have pretty generous usage. Some of them you're just paying for the actual API costs in addition, to your usage.

Jordan Wilson [00:42:36]:
So you also have to keep costs in mind. Also, the integration philosophies span from ecosystem lock in more like AWS bedrocks to universal connectivity, right, like, Zapier. So it's also like who what systems do you ultimately need these agents to pull data from and talk to? That's the other thing. You have to think of agents, you know, as like a mind map. Right? It has to be able to pull in dynamic data from multiple places, and it has to be able to execute tasks in multiple ways. All right. So what's your agent use case. Here's what I don't want you to do.

Jordan Wilson [00:43:17]:
I don't want you to look back at these 10 AI agents that I told you say, oh, this one's my favorite. This one sounds good. And go get started. Think of it. The exact opposite. You need to think about your automatable problems. I don't know. Is that a word automatable? But that's what you need to do.

Jordan Wilson [00:43:36]:
Think about your automatable problems. Because again, if you have a hammer, everything looks like a nail and you're going to get screwed. Think like so much of getting the most out of large language models out of AI powered workflows out of AI agents is really doing an honest audit of your day to day responsibilities or your team's day to day responsibilities and how you actually fulfill those. I know it sounds boring. I tell people use time trackers. What are you doing? What is your team doing? What are you actually doing? What is the work you're actually performing? Hands on keyboard, hands on mouse inside of large language models today. And then you need to find the automatable problem first, not the shiny tool. Here's what I would do.

Jordan Wilson [00:44:23]:
Start small, pick one workflow people hate, and then define a what done looks like. Right? A lot of people don't define what counts as agentic success first. And And they're just like, oh, this is good enough. Need to lock it down. Right? So you need the least privileged access, approvals, logs, and fast rollback. You need to make sure that you're using it in a safe way. If you're giving an AI agent access to your company's dynamic data, you need to make sure it's auditable, traceable, guardrails, etcetera. You also need to be able to measure weekly, not just on a per task completion, but you need to be able to measure completion rates across the board, fixes needed, time and cost save.

Jordan Wilson [00:45:05]:
Need to understand how much does it take humans to do this. Guess what? Humans make errors. Humans go through revisions. I think people think, like, people think of large language models and AI agents as something they're not. Nothing will ever be perfect. Just like there's never been a perfect, you know, human worker. No worker has gone through their entire career and never made a mistake in that spreadsheet, never made a mistake in that presentation. You know, they've no no one's ever killed absolutely, you know, 10,000 out of 10,000 sales meetings.

Jordan Wilson [00:45:34]:
Human workers make mistakes. So do AI agents. Right? So you have to be able to calculate the cost from start to finish. And then once you have and you see the positive ROI, right, maybe you think, oh, Microsoft three sixty five Copilot Studio, this is where we're gonna go. Maybe you're gonna get great ROI out of it. Maybe that's for six months from now. Maybe you're gonna get something better with using, I don't know, chat gbt agent mode or Manus or, or, you know, Zapier agents. So you need to pick one that solves your automatable problem, be able to measure it.

Jordan Wilson [00:46:10]:
And then what can scale with you? Five days. This is what I want you to do. Ready? Write this down, get out your pencils, take a screenshot, whatever. Day one, you have to define the win, right? The automatable problem, and then pick a lane, one lane outcome. So whether that's agent force, copilot studio, etcetera, don't test one thing across five different platforms. It's not going to work. Okay. Day two need a shortlist and smoke test.

Jordan Wilson [00:46:43]:
So try two platforms. All right. So not five, two. All right. Eventually get in one lane, but you need to test, try two platforms on one real world example. You need to know the setup, the accuracy, the workflow, go back, look at the observability traceability. Day three, build the MVP. So connect only the needed data and apps.

Jordan Wilson [00:47:06]:
Okay? And then confirm usable, usable by owner. Make sure has the right access to the data. Make sure the inputs are correct. Make sure it can output or, create whatever deliverable you actually need. Alright. Day four, run five cases. Time the runs, note the fixes and the limits, and then compare it to the baseline of what your humans do. That's how you calculate the ROI.

Jordan Wilson [00:47:28]:
Then day five, you decide and expand. You you confirm the platform, you write a one page how it works, and then you start sharing with the rest of the team, the rest of the organization, and then you can add adjacent use cases across different teams or across your organization based on what already works. Very small scopes. There it is, your five day plan. We covered a lot. I went over 10 AI agents. I talked about some of the problems and opportunities as this space continues to blow up. All right.

Jordan Wilson [00:48:03]:
But I will tell you this, even though this is a longer episode and I apologize for that, but hopefully y'all found some value. There's more. All right. So if you found value in this episode, as we wrap up here, please share this with your community. Go click the repost button on the LinkedIn post. So if you're listening on the podcast, always put the the link to the LinkedIn post. Go repost this and I will share. I put together a crazy useful guide on 20 different a there are some other AI agents that I really like that didn't make the top 10.

Jordan Wilson [00:48:34]:
Right? So make sure you go repost this. I will share that with you. Also, we've had a lot of great agent content and some good interviews, over the last year or so. So go check out episode five ninety, which is agents, LLMs, or algorithms, a playbook for choosing AI. Go listen to episode four fifty two, which is AI agents, the future of enterprise work, and then go listen to episode four twenty two, licensing AI agents. I like this episode. What is it and do we need it? Alright. So learn from other people, not just me, some great experts that I've had on the show.

Jordan Wilson [00:49:09]:
Alright. I hope this was help so helpful. If so, please go to your everydayai.com. Sign up for the free daily newsletter. Thank you for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks y'all.