EP 395: AI Agents - Everything you need to know

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Picture this, billions of AI powered agents doing work autonomously without human intervention. Powered by large language models, doing many of the manual tedious tasks that many of us hate. This isn't some future. This is possible now. And I think especially over the last week, AI agents have been all of the buzz. So today we're going to be talking about AI agents, what they are and why everyone is suddenly talking and scrambling to figure out and implement AI agents. All right, before we get started, a quick word from our partners at Microsoft. So why should you listen to the Work Lab podcast from Microsoft? Because it's made for leaders who know they must adapt to stay ahead. WorkLab is the place to find real world lessons and actionable insights to guide you and your organization through your AI transformation. That, that's W O R K L A B, no spaces available wherever you get your podcasts. All right, thanks to Microsoft and let's dive back in. I'm excited for this one, y'. All. What's going on? My name is Jordan and this is Everyday AI. If you're new here, thank you for joining us. If you're on the podcast, as many of you are, make sure to check out your show notes. There's going to be a link. You gotta click it. It is our website. You, your everyday AI.com on that website. Literally hundreds of episodes, hundreds of hours of whatever you care about in generative AI. It's all on our website for free for you to learn. It's like a free generative AI university. And this show, it's for you, doing it every single day. The live stream, the podcast and the free daily newsletters. Let's just jump into AI agents. And you know what? I'm going to start here because when was this? A year ago? Yeah, at the end of last year, at the end of 2023, I came out with 24 bold AI predictions for 2024. Right. So I think this was probably in November. So about 10 months ago, one of the things that I claimed that would happen in 24, 2024, and these were all, let me be honest, very bold and wild predictions. And the weird thing is, many of them came true. And when I put out these predictions, you know, not a lot of people agreed with me. But I did kind of a midterm show in June and strangely enough, almost all of them had either come true or were on, on pace to come true. And one of them that had yet to kind of come to fruition. As I said, In 2024, we may see more AI agents than humans. So here we are in September 2024 and the last week has been absolutely beautiful. Bonkers for AI agents. It's like every single big trillion dollar company in the world got together and they said, all right, let's go ahead. Agents on three. One, two, three, break. And then everyone went out and started either announcing or pushing or highlighting or updating and improving their agentic capabilities. All right, so on today's show, we're going to go over some of the basics and I also, Hey, I want to hear from you. So I want to hear from our, our live stream audience. What questions do you have about agents? So everyone joining us, you know, Colby and Michael and Antones and Marie, Rolando, everyone. Cecilia, Tara, Brian, thanks for joining us. What questions do you all have? But also podcast audience, I always leave my, my LinkedIn URL and my email in the show notes. So make sure you go check that out. Reach out, let me know what questions you have about AI agents. But one thing that I want to first talk about because we're not going to be going over this over the course of today's show, is there's different kinds of AI agents as well. So there are AI agents that will either be triggered manually, Right. So I can sit here, essentially click a button and have that AI powered agent essentially go execute a series of tasks. Right. So there are manual a AI agents, there are autonomous AI agents. So those are AI agents that are essentially running around the clock. Okay. And then there's something in between. These are called semi autonomous agents. And am I making that term up? Yes, I am making up these three classifications. So if you go Google them, you're probably not going to find very much. But I think we have to separate them in those three different categories. So keep that in mind as we talk. And kind of the middle, the semi autonomous agents, that's something in between. So that could be triggered by a workflow, that could be triggered by something, a customer inquiry. It could be something that is scheduled to happen. Right. So it's those three different types. So something that is manually triggered by a human, something that is fully autonomous, running kind of around the clock, and then something that is maybe triggered by an event, a web hook, a customer inquiry, Zapier, etc. All right, so keep that in mind as we talk about everything that's going on in the world of AI agents. So let's start with an overview, because AI agents are not science fiction anymore and they're not new. We're going to go over a timeline later, but, you know, AI agents have kind of been in the conversation for actually more than a decade. Right. But it is just with the recent advancement, obviously, in generative AI, in large language models, that have thrust AI agents not just into the conversation, but also they've crept their way into every major company. The biggest companies in the United States, and therefore the world, have all highlighted and emphasized their investment in AI agents. So this is no longer one of those fringe discussion topics, like I think two years ago it kind of was, right? You had to kind of be a large language model or generative AI or an OpenAI dork like myself, you know, two or three years ago to really be talking actively about AI agents. But it's not like that anymore. The everyday person, especially here in the US you are going to be hearing about, you're probably already hearing about them this week, and we're going to go over some of the pieces of news there. But if not, you are going to be hearing about AI agents a ton. So I think it's important to set that groundwork. And here's the other thing. Like I said, they're not science fiction anymore. They're not even a fringe idea anymore. They are here, they are live, they are available, and they are. The other thing that's wild is they are both no code and low code. So even if you want to deploy these in your organization, it's not like you need to have an army of software developers. If you have someone that can understand human language and type on a keyboard and click a mouse, that is all you need to connect AI agents, whether those are semi, semi autonomous, autonomous or autonomous or manual. If you have a human that can speak to essentially an AI system and click a button, you can start deploying these for your organization today. And like I said, I think this has the potential to be both very powerful and exciting. Right? Right. As this can lead to billions of dollars literally in saved revenue for companies. Right. You have huge companies that are already using AI agents. As an example, I believe Amazon is spending nearly $100 billion a year in research and development. Right. So when you talk about the potential to save billions of dollars for companies, that's not hyperbole. That is actually what's happening. But productivity obviously can skyrocket, right. When you think of the manual tasks that you do over and over and then essentially saying, hey, why don't I train an agent to do 80% of this. Well, it's possible today, right? So there's, there's great promise in terms of productivity, business growth, new opportunities, et cetera. But then there is obviously the downside, right? The sobering reality, y'. All. And I never lied to you here on the Everyday AI show because what you hear, I think is a false narrative because it's a, it's a comfy security blanket. When we say things like, oh, AI won't take your job, someone using AI will. That's BS because that person using AI, especially if they're using AI agents, right? In theory, the work of that one human can do the work of 2, 3, 10, 20, 30 humans. Right? So there's obviously a great challenges and pitfalls when we talk about the, the, the safety and the ethical aspects of AI agents. So are they both terrifying and exciting at the same time? Absolutely. But that's why I think now is the right time to have the conversation about this. All right, so let's just, let's just jump straight into it and I want to talk about some of the latest breakthroughs and why this is especially timely now. Right? I've been thinking about having this conversation about AI agents now since day one, since I started Everyday AI, you know, more than a year and a half ago. But I think now is the right time because literally over the last 10 days, three of the most, either the largest or the, some of the most consequential companies that control how we work have gone all in on AI agents. All right, let's talk about it a little bit first. Microsoft. All right, so we talked about this, a dedicated show literally yesterday. So if you did not check out that show, make sure to go do so. So we talked about kind of Microsoft Copilot in their Copilot Wave 2. So part of this Wave 2 of Microsoft Copilot was talking about the Microsoft Copilot Studio. Essentially it is a drag and drop AI agent builder. All right, so these are customizable agents for Microsoft Copilot 365 and they have the ability to automate complex workflow across apps. Also here is the important thing, right? Because it's all about your company's data. Can you work with dynamic data? Right? Because what good is an agent if it's dumb or if it's slow, right? Or if it doesn't, doesn't have access to your company's data? That's why I wanted to start off by talking about Microsoft because this is rolling out. I believe it should be available by the end of September here, 2024. So having that contextual memory for personalized business interactions is huge. Right? And I think that's one of the things that's been, you know, that has created this kind of gap, you know, over the, the, the premise and promise of agents over the last two years when combined with large language models to actually seeing them in production. It has been this ability to. Number one, is it technically feasible? Right. Because two years ago it was pretty difficult, right? You had to have a bunch of dorks, right? People dorkier than me. You had to do a lot of duct taping in MacGyvering to make this work. Not anymore. With tools literally like Microsoft Copilot Studio. The ability with no code and low code. So what that means is, is typing something to an AI and that AI helps you build an AI agent. And you might need to click a couple of buttons here and there to connect your database, right? So maybe from SharePoint, OneDrive, etc. Microsoft's, you know, suite of Microsoft 365 products. But at that point you can integrate with both external tools, internal tools and your live data sources. This one cannot be overlooked, right? I think the Wave two announcement from Microsoft, it got some good play. But I also think that's Wave two is kind of what closed the gap. I think from the original hype around Copilot, right. When it was first announced, you know, 18 months ago until where we are today. I think this Wave two announcement really closed that gap. And one of those things is bringing this combination of large language models that can do autonomous work via AI agents and tap into real time data. All right, that's not the only one, right? This one is extremely important. Salesforce. Yeah, literally this week is the Salesforce Dreamforce conference. And we've seen Sales, the Salesforce CEO essentially say, hey, we've been a CRM company for decades. We are doing a hard pivot. We are an AI powered agent company now. Literally. That's what he said, not me. So you have to look at their new offering called Agent Force. So we're going to break this down a little bit here in a while. But y', all, I mean, Salesforce is one of the largest companies in the world, right? One of the leading tech companies. And if you are a large enterprise organization that sells something ultimately to, to customers, clients, other businesses, right? So whether you're in B2B, B2C, there's a high likelihood that you're using Salesforce, right? And there's a high likelihood Whether you are on a sales team or not, there's a high likelihood that you spend a decent amount of time in Sal Salesforce going through all of this data, you know, to help you better manage your customer relationships. So with Agent Force, these are autonomous agents for sales, customer service and marketing, with a deep CRM and data cloud integration with Salesforce. Salesforce, as well as a low code and almost no code environment for quick deployment. And also, here's the other thing, the Nvidia collaboration, right, that, that piece is huge there for Salesforce. All right, and kind of the third piece that I wanted to talk about is OpenAI. Now, obviously, OpenAI is not a company like Microsoft and Salesforce that has been dominating kind of the tech landscape for multiple decades, but I think they actually are one of the most important companies in the world. Here's why, right? I just mentioned as an example, Microsoft powered by OpenAI's GPT4, another big company that we probably use every day, right? Apple, if you're a human being living and working in the United States, you either probably use Microsoft Windows every single day, or you use Apple or Mac every single day. And guess what? Apple and Mac are both going to be powered by OpenAI's GPT4O. Right? The Apple Intelligence. Yes, they have their own kind of edge AI, small language model handling certain queries locally. But then for other queries, they're going to be sending you to OpenAI's GPT4O. Right? And we talked and we, and we've heard even from Microsoft, their willingness to integrate Open AI's newest model, Open AI 01. Okay. And this is a. You know, it was formally codenamed Q Star, then it was codenamed Strawberry. Right? So if you've heard about Q Star or Strawberry, this is a model that is completely different. All right? And also, hey, can we stop calling this GPT01? That's not its name. All right, so there is the GPT class of models, right? And then there is the reasoning class of models, which is the O1. All right, but so many companies, yeah, I mentioned Microsoft and Apple are actually using OpenAI's technology. But there are, uh. I wish I had an official count, I would venture to say tens of thousands or hundreds of thousands of companies that you probably use fairly often, right? You're not using tens of thousands of them, but there are thousands and thousands of companies that we all use all the time that are actually powered by OpenAI's technology. So when you're using, you know, you think you might be using, oh, this AI powered real estate app. Guess what they're using GPT4O, right? So we also have to pay attention to what OpenAI is doing in this agentic or AI powered agent space. And for that I think we have to look at their recent model, this strawberry QAR01, right? So last week OpenAI released 01 Preview and 01 Mini. These are again a new class of models and we don't even have access to their most powerful model which their most powerful reasoning model which is 01. Okay, so we essentially have a one preview and a one mini. But this is an agentic model that is capable of reasoning, it is capable of thinking under the hood. And that is one key aspect that can bring this agentic workflow to thousands of the softwares and services that we all rely on every day. So think of how I said, right? As an example, Open Microsoft has their new Copilot Studio Agents Salesforce has gone all in with Agent Force, right? This is not some one off trend. We are going to be seeing this probably on every single big piece of software. You're going to see agentic capabilities and there's a good chance they might be powered by OpenAI. So hey, this is fresh off the press, right? Couple hours ago Sam Altman tweeted incredible out performance on Goal three, even though it took a while. And then he left a link to an OpenAI Technical Goals Go blog post. And here's what Goal 3 is. Build an agent with useful natural language understanding. All right, I'm going to read this quickly. We plan to build an agent that can perform a complex task specified by language and ask for clarification about the task if it's ambiguous. Today there are promising algorithms for supervised language tasks such as question answering, syntactic parsing and machine translation. But there aren't any more. But there aren't any. For more advanced linguistic goals such as the ability to carry a conversation, the ability to fully understand a document, and the ability to follow complex instructions in natural language, we expect to develop new learning algorithms and paradigms to tackle the these problems. So this blog post here that Sam Altman just shared about is an older blog post, but he is clearly hinting that with OpenAI's O1 model, they have essentially outperformed this goal right where they said we expect to develop new algorithms and paradigms to tackle these problems, which is the ability for an AI agent to understand a document and follow complex instructions in natural language. All right, so we're going to be talking more about open AI Here in a bit. All right? And hey, live stream audience, if you didn't already get your question in, try to get it now. I'm going to tackle them. Try to tackle them at the end. All right, so let's talk about what actually makes an AI agent, right? Like, what the heck is an AI agent? So I kind of wanted to start off with some of these recent examples because it's actually nuttier than a squirrel on keto that all of these things from, you know, Microsoft Salesforce and OpenAI have happened over the course of like, six days, right? That can't be a coincidence on where the industry is heading. But I think we also have to just talk about what makes an AI agent. All right, this is my list, y'. All. This is six core functions of an AI agent. There's. There's other lists out there. Essentially, what defines an AI agent, right? Because it just sounds like a buzzword. Right? And in the same way that companies wanted to throw out just AI or generative AI or large language models, right. They try to spit out those buzzwords as quickly as possible on their earnings calls and quarterly forecasts. Right? Now you're going to hear the same thing with AI agents. So what the heck is an AI agent? And what's the difference between an AI agent and a large language model? Well, first of all, that line is probably going to blur as large language models add to their capabilities. But I'd say here are the six core functions that kind of constitute an AI agent. So you got to check all of these box boxes. All right? Number one is it needs to be powered by a large language model which enables it to have natural language processing. What that means is the average human needs to be able to talk or type to an AI agent in plain English or plain whatever language you speak, and it needs to be able to understand human language. That's number one. Right. If you have to write Python on the front end for something to happen, in my opinion, that is not an AI agent. Can it be an agent? Sure. Right. But not by my definition. It needs to be powered by a large language model, and it needs to have NLP or natural language processing, needs to understand humans. Number two, it needs to have tool interaction. It needs to be able to use outside tools. All right? Number three, it needs to have the ability to plan on its own. Right? To handle complex tasks, an AI agent needs to be able to plan how they plan to do that. Right? And. And sometimes you might see this chain of thought reasoning, which is kind of what we see with, you know, StrawBerry or open AI's.01 model. Number four, you need, it needs to be able to have memory and, or access to company data, right? An agent is not useful or even I wouldn't consider it an agent if it doesn't have a memory or the ability to store or access your company's data. And that might come through, number two, right tool interaction, but it needs that. Number five, this is a big one. It needs to be able to actually execute tasks on your behalf. Again, whether that is a manual trigger, autonomous trigger, semi autonomous trigger. All right, it needs to be able to actually execute something, not just, oh, here's how you would execute this in theory, right? That's one of the things that, you know, one of the lines in the sand, so to speak, that differentiates a large language model. With an AI agent, can it actually execute a task? Right. And now we're seeing that with, especially right now with Agent Force and with Microsoft Copilot Studio. There's other offerings that we'll be talking about here in a bit from Google, Meta, et cetera. But it needs to actually be able to execute a task, which is one of the new things that we saw from Microsoft 365 Copilot in their studio. Essentially, their AI agent builder is it can now execute tasks on your behalf. You have to give it access or you have to kind of, you know, click, yes, you can execute this task, but it can. And then number six, learning and adaptation, right? That's the other big one. If, if an agent cannot learn and improve, right? And normally if this is powered by a large language model, it can learn and improve, but it needs to do that. All right, let me recap those things quickly. One, large language model and natural language and natural language processing. Two, it needs to have tool interaction or outside tools. Three, the ability to plan or chain of thought reasoning. Four, memory and or access company data. Five, the ability to actually execute tasks. And six, the ability to learn and adapt to become better. All right, we have to take a real quick break to tell you about Worklab from Microsoft. So why should you listen to the Worklab podcast from Microsoft? It explores the questions business leaders are asking. How can they guide their organizations on their AI adoption journeys? How can the technology help them create new products and business models and maximize value? How should they help help their teams reskill for this new era of work? And why is it important to be completely transparent about when and how you utilize AI? Find the answers on Worklab. That's W, O, R, K, L, A B no spaces available wherever you get your podcasts. So let's get back to the show. All right, those are the six core functions and that is what differentiates AI agents from large language models. Because right now, large language models, with the exception, I think, of what OpenAI's Zero1 model will do in the future, because right now Zero1 does not have tool access, right? If I'm being honest, if it had access to all the tools that GPT4O has access to, for example code interpreter, slash data, advanced data analysis, if it had access to, you know, Dolly, even though I don't think Dall E is that great. If it had access to the ability to upload files, if it had access to the ability to browse the web via the browse with bing integration. If, if 01, if the O1 model had access to that right now, it would be an agent, right? It would be an AI powered agent. Right now it doesn't have access to those things, although OpenAI said that should be around the corner. So, you know, essentially you have the two different pieces with chat, GBT and OpenAI. Right now you have the GPT4 model which doesn't have, you know, number three on this kind of the ability to plan chain of thought reasoning and tax, tax execution number five. But you know, GPT4 models don't have that, but the new kind of reasoning model does. So OpenAI has all the pieces, which is why, you know, I think that not so cryptic tweet from, from Sam Altman means a lot more than we think. All right, so like I said, the difference large language models, text generation, right? And agents are decision making, execution, completing task, real world, being able to learn and adapt. All right, let's go over a very, very brief history, very brief history, all right? Because I, I, I don't want this to accidentally be an hour long podcast, all right? You can't really talk about modern day AI agents without first shouting out LangChain. All right? LangChain was very early to this game. So in October of 2022, LangChain launches launched and you know, this was essentially, you know, very ahead of its time. Don't get me wrong, but think of this as way it was kind of like duct taping and MacGyvering, right? So you could tap into different large language models and then, you know, kind of string together creating a workflow to create a sort of agent. So again, it was very ahead of its time. But you can't not talk about LangChain. So and then in November 2022, OpenAI launched GPTs, right? Oh, no, wait, that was 23. Sorry. So first LangChain in quarter three introduced LCL for. That's essentially their kind of language for flexible agent creation. Then we saw in November from OpenAI the ability to create GPTs. So again, that's not an AI agent, but that's laying the framework, right? So with custom GPT, that was the ability for essentially agentic task, right? Not an agent, but more of an assistant, right, where you could, you know, kind of make a custom version of a large language model, update, you know, upload some of your data. It has access to all of those tools and it can complete singular tasks, Right? It can't really do that chain of thought reasoning and, you know, run autonomously or semi autonomously, but GPTs were definitely a step in that. All right, and then we can fast forward to early 2024. So Nvidia showcased their AI agent hardware acceleration. So you can't skip over Nvidia's involvement in this. And then we go to April 2024. Meta AI with llama3 has started to integrate and slowly tease out its agentic workflows. The same thing with Google at their IO conference. Kind of teased and previewed agent building capabilities there. And then that brings us to current day in September. In the last, like I said, seven days, we've seen OpenAI announced the O1 model, a preview of agentic reasoning once it has access to all the things that the GPT models have access to. Then we saw the Microsoft Copilot Studio Wave 2 with these new enhanced agent capabilities. And then we saw Agent Force from Salesforce that is marking a shift from one of the largest, you know, software tools in the world, going from a CRM company to an AI agent company. It's a very brief history and just a very brief recent history, all right? But AI agents have been around for a very, very long time. All right, let's talk about how they can change work. Well, if you're still listening to this podcast and you don't see the potential for how they could under change work, how they could change work, it's like y', all, you gotta, you gotta think, you gotta look at the writing on the wall, right? And also look at the largest companies in the world, right? So I, I, I already talked about Microsoft, right? I already talked about Apple, right? Apple with their Apple intelligence. So Apple and Microsoft, they control the devices that we use. And Microsoft has already all out said, yes, AI agents, they're here, they're hu, they're A big part of what we're doing. Apple is not there yet because they're like two years behind literally every other company. But we have Apple intelligence coming out. So presumably you will start to see some type of autonomous or semi autonomous workflows in the future with Apple. Nvidia, right? I'm going over the largest companies in the world, Nvidia, they create, they are the engine, right? They are literally the engine driving AI agents and how we all work in the future. Google, like I said, Google at their, at their IO conference announced AI agents, right, in their Vertex AI agent builder. So as they continue to approve, they're very capable Gemini models. I think Gemini models are great on the back end for developers in their AI studio. Not so great on the front end for the average user, right? But with the Vertex AI agent builder, again, one of the largest companies in the world. Then you have Amazon, right? Amazon is investing billions of dollars into large language models. They have their own large language model platform, Amazon Q. They're working on simple agenda workflows and then meta as well, right? Those are literally the six largest companies in the United States and six of the largest companies in the world. And they are investing their dollars and their people into AI agents in some way, shape or form. So you have to see the writing on the wall. You have to always follow the money, and the money, the time, the attention is all going toward AI agents. So this will greatly impact how we all work. And it is unfolding now before our very eyes. All right, let's talk about a couple example business use cases, right? I hear y' all when you reach out to me on LinkedIn and send me emails, I always appreciate that. And you know, you always say, hey, we gotta hear, we gotta hear more business use cases, right? So I'm just gonna give some examples. So Salesforce Agent force, right? So they did a video on this. We'll leave that video in the newsletter. It was a short video, five minutes that kind of shows how this kind of no code or low code agent building works in their agent force. So in this example, you can use your CRM data, you know, build the parameters, you build the guardrails. Again, you don't have to be super technical. It is drag and drop. So on my screen here again I have like a left side, right side split. So on the left side with natural language, you kind of set the parameters in the rules of how your AI agent can respond. And then what happens is it is connected to your live Salesforce data and then you can essentially create A chat, right? It's funny. Chatbots were so ahead of their time, but they were so useless, right? Because with chatbots pre large language models, you had to set all of these defined conditions which maybe, you know, accounted for like 1% of conversations that might actually happen on a chatbot, but now it is flipped. I think with natural language processing in large language models now, you can probably hit on like 99% of all customer inquiries, right? But with Agent Force, you can essentially tap into your Salesforce data, create a simple agent that you can then put on your website. And in this example that Salesforce had, you know, essentially a customer is asking like, hey, what? You know, they had a question about their order, Salesforce. The agent answered it, and then they said, hey, I need, you know, installation. I need installation. And then they said, what about next Friday? Right? So they weren't saying, hey, what about, you know, Friday, September 27th? They just said next Friday, right. So you have to have that natural language processing in a large language model to be able to take nuanced conversation and translate that to data and then connect it to your database. Right? And then the Salesforce agent gives the options, and they said, hey, we have the following available times on this date. And then they gave a couple options. The again, the person just says 230, and then that's one of the options. And then the agent force agent schedules it and then updates the CRM accordingly. Right? So this is huge. Something's. I mean, I know this is a super simple example, right? But customer service is. I don't, you know what? I don't see how humans in the future, in the very near future are going to be the driving force behind customer service. It doesn't make sense anymore, right? It. It really doesn't. When you have, you know, if this works, right? This is, this is all very new. You got to take this with a big grain of salt. But then you have similar offerings from, you know, other tools, softwares, like. Right, I said Microsoft, Copilot Studio, same thing. But y', all, that is going to completely change how customer service gets done. Let's talk about sales. You know, kind of sales and marketing. Zapier. Zapier has. They call them AI assistants, but they're really great. Zapier Central we've talked about on the show before. Similarly, these are more like semi, semi autonomous, but with drag and drop. So you have to connect to other third party, you know, software. So maybe as an example, you don't use Sales Flare, Salesforce. Maybe you use something else, right? But you can build kind of these semi autonomous agents, drag and drop, with natural language, tapping into a large language model via zapier. Right. So, oh, when we get a, you know, an inquiry on our website, you know, you can kind of set some simple rules with natural language. If someone chooses option A, you know, you should send them this email, but write it in a way that takes into account all the information they put on the form. Right? So you can essentially have a combination setting guardrails, setting conditional triggers, and then, you know, tapping into literally almost any software or service on the Internet. So when you have this, you know, kind of AI powered agent, tapping into a large language model and your data, your services, right. You can see the future. Coding. Coding is another one, right? We're talking about real business use cases. I know I asked you all, I got mixed responses, right? Do you want to see more on AI coding, AI software development, Right, because this is changing as well. And I think that this obviously makes the job easier for people who are already, you know, in software development or engineers, people doing coding, Python, right, etc. These tools like Replit agent, cursor AI, right, being able to code with AI. Devin, you know, the AI software engineer from Cognition, right, which OpenAI prominently featured in its 01 announcements, right. So those three companies alone, there's a lot more. But these are probably three, you know, Cursor AI, Replit Agents, and Devin from Cognition are probably three of the more prominent. These are AI powered software agents, right? So yes, you have these agents that will in theory be able to do any, a little bit of anything and everything. But then you also have more niche and targeted AI agents. But then also. So yes, this changes what's possible for software developers, engineers, et cetera. But then this also gives new capabilities, right? That's the other thing with AI agents, they bring new capabilities to anyone else. Because I can go right now on Cursor AI as an example, and I can, with natural language, I can say, hey, build me a program that does this, I need it. And then probably after, you know, three or four or five prompts, I actually have a piece of software that I created that solves one of my own problems. Right. Um, so the capabilities in specific categories of work are about to drastically change. So yes, there are general purpose AI agents, right. But then there are niche or skill specific AI agents as well. And I think that Replit, Agent, Cursor AI and Devin are great examples of those benefits. Right here as we're wrapping up, you got to be able to see the Benefits, right? This is 24. 7 non stop work. Right. If you're, if we're talking more on the autonomous agent side. Salesforce itself said they see billions in the next year. Billions with a B, y'. All, I'm not crazy. People think I'm crazy. When I come off with these hot takes. There's going to be more AI agents than human. And people laugh and they're like, this guy's dumb. And then Salesforce says it. Salesforce says within a year they see billions of their agents. Is that part marketing, part, you know, you know, dreamer vision? Sure, right. This happened at Dreamforce after all. Is it a reality? Absolutely. Could it, or sorry, could it be a reality? Yes, Hex, yes, it could be. Right? Because now I can go create today, 10, 20, 30, 40 agents. You literally have people and softwares that are agents that are creating other agents. So literally autonomous agents working around the clock creating other autonomous agents. You have agents interacting with each other. Again, no longer science fiction. Two or three years ago, you know, us dorks were sitting around on Reddit and quoruming like wouldn't this be cool? And now it's. It's reality. Right? So the benefits 24, 7, working with your data on guardrails, you set up using your up to date knowledge. And this also helps non technical users. That's the other big thing, right? Because again, technically you had artificial intelligence powered agents now for probably more than a decade. But with generative AI, it democratizes, it lowers the bar. So you no longer, you know, 10 years ago, yeah, there was artificially intelligent agents, you know, probably more on the manual or semi autonomous side. Right. But now anyone can do it. You could have started this process at the beginning of this podcast and probably could have already created five by now. That's also probably a key for me to go a little faster. Right? All right. We can't talk about this without talking about the challenges and limitations. All right? So the quality of reasoning is huge, really. Defining this agent computer interface, or ACI as it's sometime called, in designing effective interfaces for tool utilization, that's a challenge as well. But I think what we're seeing with Agent Force and Copilot Studio really is huge. And we'll see with Vertex AI Agent Builder from Google how that goes. But also there's model limitations, right. There's biases right now in these large language models that are in theory powering these AI powered agents. So that's a huge challenge. And also ethical and safety. Right. Especially as AI agents start to learn and Adopt or sorry, learn and adapt, right? And then as we start talking about multi agent environments again, I'm not a doomsdayer out here talking every day about Skynet and Terminator, but you got to think about that, right? Hey, you set up some maybe weak guardrails and then you, you know, set out a system of, you know, 50 autonomous AI agents that can all talk with each other, powered by, as an example, maybe OpenAI's 01 model, right? Bad things could, in theory happen, right? If you don't have enough human in the loop, which I know y', all, let's be honest, sometimes that is an exaggerated way of saying, hey, us as humans are babysitters for AI. But if you don't have enough human in the loop at the right point, a series of multiple autonomous agents working together in the future could obviously be very unsafe. All right? If you don't constantly have humans overseeing them, if you don't have strict guardrails in place and constantly doing QA on these agents, the future can also be very scary, right? So I don't want to, I don't want to skip over bias, stereotypes, safety, ethics, right? Not even talking about job loss, because I don't care what anyone says, AI is ultimately going to take away way more jobs than it creates. Sorry. It's not me being a pessimist, that's me being a realist. Right? That's why the largest companies in the world have all invested billions of dollars into generative AI, into GPUs, into AI agents, right? Yet they're laying off tens of thousands of employees with record high profits. Right? I don't know why people don't, you know, I know we need to be optimists as human beings when we talk about AI and our jobs and career and the meaning of work. Right? But you also have to be a realist. Wall street hates employees. Wall street loves profits. And Wall street is really going to start to love AI agents, right? Keep an eye on Microsoft stock, Salesforce stock in the, in the next year or so. You'll see what I mean. All right, then the future and what's next? Well, I don't know what's next. All I know is it definitely has to do with AI agents. Like I said, out of the top six companies in the us so aside from Apple, every other company, Microsoft, Nvidia, Alphabet, Amazon and Meta have all come out and publicly said that they are either investing in or investigating AI agents or they are all in, right? And then we talked about Salesforce as well. One of the largest companies in the world that touches so many other Fortune 500 companies. Right. Like the majority of large enterprises are using Salesforce. I don't even know who Salesforce's biggest competitor is. Right. Who knows? I mean, I probably know a couple of them, but you get what I'm saying. The biggest companies that dictate how we work all going all in on AI agents. All right, let's wrap this up y'. All. So some key takeaways here. We are on the cusp of this big shift from yes, AI to generative AI to AI agents, right? And you know, you can't really talk about that shift without talking about AGI and you know, are more capable models and AI agents kind of wanted this next step that gets us to this artificial general intelligence or when AI is much smarter at every task than any human being in the world and, and can do all of these tasks without really much human intervention. Right. And I think agents are a step to get us there. Whether you are rooting for AGI or not, it doesn't matter. But we have to talk about that. So we also have to talk about the duality of AI powered agents. If I'm being honest, I was shocked over the, over the last week that all of this is happening all at once. And that's why I said we got to do a show on this, right? I think a year ago was still too early, but now the writing is on the friggin wall, y'. All. You gotta pay attention to it, all right? And we have to talk about the duality and we have to business leaders and decision makers. You have to do AI agents and kind of this implementation in the right way. You have to prioritize humans, you have to prioritize safety, guardrails, data, etc. You can't skip over this in the mad rush to be the first big company to implement something from agent force to implement, you know, the, the biggest, you know, AI agent from Microsoft copilot. You don't have to do that. You should be acting now, right? If you don't already have generative AI in place in large language models, you're kind of screwed. Right? You should already be having that conversation about these autonomous workflows, AI powered automation and AI agents. You have to be having those conversations whether you're implementing it tomorrow. That's not what I'm telling you to do. You have to be planning for it now. All right, so let's wrap. Got a couple of questions here. Let's see, let's see. That's part of doing this as a live stream. I know I ramble on sometimes, but I think even with everything that's going on with AI and we're spending so much time now talking to AIs, I think it's important to have a human conversation. So, you know, podcast audience, you might get kind of annoyed when I, you know, ask questions so much of our live stream audience. But I want this to be a place and hey, podcast listeners, come join us. I want this to be a place where we have real human to human conversation. Right. I want this to be a place where we can explore and learn together. So a question here from Kobe. Thanks for joining us. He says, I'm curious about your thoughts, Jordan, on building agents inside of Zap Central. Colby was, you know, reading ahead. Have you built any and have you found Zap effective? So when Zapier Central first came out, I went in and played around with it. So I did build one or two basic workflows. To tell you the truth, I haven't gone back there since and I'm kicking myself because I really should be right. We pay for Zapier every month and I'm barely tapping into it. But I do think, especially for companies that maybe don't have Salesforce or don't want to go all in on this Agent Force, because the thing I didn't mention is I believe the pricing for agent force is $2 per conversation. I'm not sure about that go to market strategy, but that's why they probably, you know, have a bunch of smart people telling them that that's the way. But I do think Zapier Central and Microsoft Copilot Studio, their AI agent builder, are probably the two most robust kind of AI agents that are ready to go. I do think end to end, Microsoft Copilot Studio is obviously a little more capable because it works with your real time data out of the box. Whereas in Zapier Central, if you're building kind of these agentic flows, you know, you are, I won't say duct taping it. Right. It's a little more seamless when it is built into the software that you're using on your computer versus when you're having to kind of put the pieces together. All right, Marie, asking devil's advocate here again, if AI is going to, quote, unquote, take over, what are humans going to be able to do? Good question. Right? Yeah, I think that's important to talk about and I kind of wrapped up the show as much. Right. Talking about that. What is the Future of work. And I think it is a more responsible use of humans in the loop. I think hopefully, right. Who knows, maybe we will actually see some companies make the ethical decision. I think unfortunately many companies as they figure out large language models across their organization, top to bottom, you know, as they figure out AI agents, I think so many companies are going to rush to just fire employees by the thousands. We've kind of already seen that. Right. From big tech companies over the last year. I think that's going to happen a lot. It's going to happen in mass, unfortunately. Again, I'm not being pessimist, I'm not being a pessimist. That's following the data, that's following the writing on the wall. So what are humans going to do? Well, hopefully that more creative and strategic thinking, you know, who knows, maybe we'll see this being commonplace, having, you know, four day work weeks as an example. Right. Who knows, right? But I don't know what the future of humans role in this as large language models get smarter and more capable and as we see AI powered agents. But I do know what we've traditionally done in the past decades where you are rewarded for your domain expertise, right? You're, you're rewarded for all of these facts and decision making that you have in your head around, you know, your specific industry. I'm not saying that that's going to be gone, but that is going to be greatly deprioritized. It doesn't matter what you know about marketing, it doesn't matter what you know about shipping and logistics, right? It doesn't matter because these AI agents, when they can access your company's data, when they can access the, you know, entire history of the world in theory, right? And they can adapt and access your company's data and learn and change, right? That domain experience you have up in your brain all of a sudden is much less valuable. So I do know the future of work requires us to think more creatively and strategically. And I think almost all of us in the same way that, you know, now who would have thought 30 years ago that essentially every single knowledge worker here in the US would be working, quote, unquote on the Internet all day. I think in the very near future we are going to be working in and around AI all day. So whether that's Microsoft Copilot, whether that's, you know, you know, Agent Fours or you know, from Google Vertex AI agents, whether you're building your own AI agents, I will assume the majority of workers in the coming years will be working around generative AI in large language models in some way, shape or form. Just like we're all right now working around the Internet. Last question and we're going to wrap it, Tara. Asking how can we ensure fairness in detecting AI agent use in the workplace and schools and prevent false accusations of relying on AI without proper evidence? That's, that's, that's a great question, Tara. And I have no clue. I have no clue. Maybe this goes to the question that Marie just said. What are humans going to do? Well, humans, we humans, when we need to prioritize ethics, safety, getting rid of bias. Right. Fairness, equity and equality. Right. These are the types of things as AI agents and large language models take away from maybe the majority of our manual mundane knowledge work tasks. These are the big issues we have to cover. Great question, Tara. And don't worry, we're going to be here every single day helping all of us uncover those next steps of how do we work in the future. Well, at least you know a little bit of how we're going to be working in the near future, but we're going to continue to tackle it here on Everyday AI. Thank you for joining us. If you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. We're going to be recapping today's show. Yeah, I know it was a long one, but I think this was an important discussion to have because I would not be surprised if we have this same conversation at the same time next year. I wouldn't be surprised if there are billions of AI agents out in the world. That wasn't just my bold prediction. You heard it from Salesforce as well. But all I know is the future of work is generative AI and and the largest companies in the world are going all in on AI agents. Thanks for tuning in. We'll see you back tomorrow in Everyday for more Everyday AI. Thanks, y'. All.

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