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The Rise of AI Agents: What You Need to Know
Artificial Intelligence (AI) agents are rapidly gaining attention as pioneering developments in natural language processing and generative models bring their potential into sharp focus. These agents make use of advanced AI capabilities to interact with business databases in conversational terms, simplifying tasks and reducing the need for human intervention. With companies like Microsoft introducing tools like Copilot Studio, and platforms like Zapier facilitating AI-powered agent creation, such technology is no longer the domain of technical experts alone.
Revolutionizing Customer Service, Sales, and Marketing
AI agents are making a significant impact in fields such as customer service, sales, and marketing. They can handle customer interactions, schedule appointments, update CRMs and manage an array of tasks via natural language recognition and third-party integrations. This level of automation means continuous, 24/7 operations, and a significant leap in productivity and efficiency.
Embracing a Future with AI
As the trend is set to continue, the widespread proliferation of AI agents predicts a future where such entities could eventually surpass their human counterparts in many sectors. However, this shift towards AI dominance is not without challenges. The quality of reasoning, interface design, intrinsic biases in large language models, and ethical concerns in multi-agent environments are issues that need addressing.
Navigating the Challenges and Controversies
Although AI has the capability to create new roles, the potential job loss with the adoption of AI is a subject of concern. While technology advances, human oversight remains essential to ensure safety and proper functioning. Achieving a balance between the benefits of AI integration and the preservation of human roles demands careful strategy and ethical decision-making.
AI and the Corporate World
With AI agents now accessible even to individuals with minimal technical skills, major US corporations are not the only entities investing heavily in these advancements. The global economic impact is significant, with the potential for considerable savings in big companies’ revenues. A testament to this, Amazon allocates around $100 billion each year in research and development.
Staying updated with AI Developments
With rapidly advancing technology and consistent legal and ethical developments in the AI field, it is imperative for businesses to stay up-to-date. Recent examples include the introduction of legislation against AI misinformation in California, the AI infrastructure collaboration between BlackRock and Microsoft, and the Global AI Safety Summit hosted by the Biden Administration.
Preparing Businesses for a Future with AI Agents
Current industry trends point towards a transition from traditional AI to generative AI and AI agents, potentially setting the stage for artificial general intelligence (AGI). Major corporate players including Microsoft, Salesforce, Google, and Meta are already implementing agentic capabilities in their software offerings.
Ethical Priorities in the Era of AI
In conclusion, as implementations of AI agents and their capabilities become commonplace in everyday work activities, it is crucial to prioritize ethics, safety and an equitable approach. It's important to balance AI application with a deliberate focus on removing bias, ensuring fairness, and preserving human roles. With careful preparation and a focus on ethical concerns, businesses can leverage the power of AI agents effectively and responsibly.
Topics Covered in This Episode
1. AI Agents Explained
2. Future of AI Agents
3. Corporate Adoption
4. Economic Impact
5. Ethical Concerns
Podcast Transcript
Jordan Wilson [00:00:16]:
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. 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.
Jordan Wilson [00:01:12]:
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, your everydayai.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 newsletter.
Jordan Wilson [00:01:41]:
So make sure you go to your everyday ai.com and sign up for that free daily newsletter. And you know what? We're not dragging this on anymore. We're finally today is the absolute blast day. Right? Yeah. We've been dragging this on. But our thanks, thanks a million giveaway if you haven't signed up to celebrate a million downloads. I think we're ending it at midnight tonight Central Standard Time. So, giving away a year of whatever your favorite large language model is.
Jordan Wilson [00:02:06]:
The base subscription, the 20 to $30 a month price. So whether that is, ChatGPT, Claude, Gemini, etcetera, go make sure sign up. Takes 10 seconds. You get a link, refer your friends. It's not too late to enter, the contest. Alright. I'm excited to talk about AI agents. It's, a topic that we don't talk about maybe enough on the show.
Jordan Wilson [00:02:30]:
So, before we dive in, we're going to start as we do every day by going over the AI news. So California has taken some bold steps against AI generated election misinformation. California governor Gavin Newsom has just signed 3 significant bills aimed at combating the use of artificial intelligence to create misleading images and videos in political advertisements, especially with the 2024 election approaching. So the new law makes it illegal to create and publish deep fakes related to elections within 120 days before election days 60 days after, imposing civil penalties for violations. So courts will have the authority to halt the distribution of misleading materials, enhancing legal resource against deceptive practices. Large social media platforms will be required to remove deceptive content under a pioneering law set to take place, set to take effect next year positioning California as a leader in regulating AI usage and political, political context. So political campaigns must disclose if they are using AI altered materials in their advertisements promoting transparency to voters. So Newsom's actions come in response, to recent incidents of manipulated content, including a controversial video alter featuring altered images of vice president Kamala Harris shared by Elon Musk, as well as, AI generated images, shared by, former president Donald Trump.
Jordan Wilson [00:04:02]:
In related news to that, California governor Gavin Newsom also signed legislation to protect actors and performers from unauthorized use of artificial intelligence, allowing them to back out of contracts that may permit studios in California to create digital clones of their likeness. So that's a pretty big one as well. Alright. Our next piece of AI news, we barely snuck this in the newsletter yesterday as it went out a little late, but BlackRock and Microsoft have united for a $30,000,000,000 AI infrastructure fund. In a significant move to bolster AI capabilities, BlackRock and Microsoft have have announced plans to establish a fund exceeding $30,000,000,000 yeah. A billion with a b. $30,000,000,000 aimed at investing in AI infrastructure, including data centers and energy products, projects. So the newly formed Global AI Infrastructure Investment Partnership, that's a mouthful, will focus on enhancing AI supplies chain, supply chains and energy sourcing to support the growing needs of AI technology.
Jordan Wilson [00:05:05]:
The fund is expected to mobilize up to $100,000,000,000 in total investment potential, factoring in debt financing, which could significantly impact the, AI landscape. So, also worth noting that, MGX, an Abu Dhabi backed investment company, will serve as a general partner, while NVIDIA will contribute its expertise in AI chip technology further strengthening, the fund's capabilities. Alright. Last but not least, the Biden administration is set to host a global AI safety summit amid regulatory challenges. So the Biden administration is making headlines this morning by organizing a global safety summit focused on artificial intelligence, highlighting the urgent need for regulation in this rapidly evolving field. This initiative comes as congress struggles to establish effective oversight for AI technology. So, this conference will be held, in November of this year in San Francisco, and the summit aims to foster global co op cooperation for the safe and trustworthy development of AI with participation from member come countries including Australia, Canada, the EU, Japan, and obviously, the United States. Alright.
Jordan Wilson [00:06:21]:
We're gonna have much more on that and all other stories in our newsletter. So make sure if you haven't already, please go to your everyday a i.com. Alright. That was actually a lot. I'm sorry. That took a hot minute and a half, but let's just jump into AI agents. And you know what? I'm gonna 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.
Jordan Wilson [00:06:49]:
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 is 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 bonkers for AI agents.
Jordan Wilson [00:07:36]:
It's like every single big $1,000,000,000,000 company in the world got together and they said, alright. Let's go ahead. Agents on 3. 1, 2, 3, break. And then everyone went out and started, either announcing or pushing or highlighting or updating and improving their agentic capabilities. Alright. So, on today's show, we're gonna go over some of the basics, and I also hey. I wanna hear from you.
Jordan Wilson [00:07:59]:
So I wanna hear from, our our our livestream audience. What questions do you have, about agents? So everyone, joining us, you know, Colby and Michael and, Antonis 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 gonna be going over this over the course of today's show, is there's different kinds of AI agents as well.
Jordan Wilson [00:08:37]:
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 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.
Jordan Wilson [00:09:09]:
I am making up these 3, classifications. So if you go Google them, you're probably not gonna find very much. But I think we have to separate them in those 3 different categories. So keep that in mind as we talk in. Kind of the middle, these 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.
Jordan Wilson [00:09:35]:
Right? So it's those 3 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 webhook, a customer inquiry, Zapier, etcetera. Alright. 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.
Jordan Wilson [00:10:03]:
We're gonna 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 2 years ago, it kind of was. Right? You had to kinda be a a large language model or generative AI or an open AI dork, like myself, you know, 2 or 3 years ago, to 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 gonna go over some of the pieces of news there.
Jordan Wilson [00:11:09]:
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.
Jordan Wilson [00:11:37]:
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? As this can lead to 1,000,000,000 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 a $100,000,000,000 a year in research and development. Right? So when you talk about the potential to save 1,000,000,000 of dollars for companies, that's not hyperbole. That is actually what's happening.
Jordan Wilson [00:12:37]:
But and and you know, productivity, obviously can skyrocket. Right? When you think of the manual task 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, etcetera. But then there is obviously the downside. Right? The the the sobering reality y'all, and I I 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.
Jordan Wilson [00:13:21]:
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 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. Alright. So let's just let's just jump straight into it. And I wanna 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 1.
Jordan Wilson [00:14:03]:
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, 3 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. Alright. Let's talk about it a little bit. First, Microsoft. Alright. 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.
Jordan Wilson [00:14:43]:
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. Alright. So these are cost, 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.
Jordan Wilson [00:15:26]:
Right? Or if it 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 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 2 years when combined with large language models to actually seeing them in production. It has been this ability to number 1, is it technically feasible? Right? Because 2 years ago, it was pretty difficult. Right? You had to have a bunch of dorks. Right? People dorkier than me.
Jordan Wilson [00:16:13]:
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, etcetera, 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.
Jordan Wilson [00:16:56]:
Right? I think the wave 2, announcement from Microsoft, it got some good play, but I also think that's wave 2 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 2 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. Alright. That's not the only one. Right? This one is extremely important. Salesforce. Yeah.
Jordan Wilson [00:17:41]:
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 gonna 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.
Jordan Wilson [00:18:16]:
Right? One of the leading tech companies, and if you're a large enterprise organization that sells something ultimately to to customers, clients, other businesses. Right? So whether you're in, b to b, b to c, 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 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. Alright.
Jordan Wilson [00:19:09]:
And kind of the 3rd 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 open AI's GPT 4 o. 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.
Jordan Wilson [00:19:54]:
And guess what? Apple and Mac are both going to be powered by OpenAI's GPT 4 o. Right? The Apple intelligence. Yes. They have their own kind of edge AI, small language models handling certain queries locally. But then for other queries, they're going to be sending you, to OpenAI's GPT 4 o. Right? And we talked and we and we've heard even from Microsoft their willingness to integrate OpenAI's newest model, OpenAI01. Okay. And this is a, you know, it was formerly code named QStar, then it was code named Strawberry.
Jordan Wilson [00:20:35]:
Right? So if you've heard about Q star or strawberry, this is a model that is completely different. Alright? And also, hey, can we stop calling this GPT01? That's not its name. Alright. So there is the GPT class of models, right? And then there is the reasoning class of models, which is the o one. Alright. But so many companies Yeah. I mentioned, Microsoft and Apple are actually using OpenAI's technology, but there are, 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.
Jordan Wilson [00:21:11]:
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, GPT-4 o. 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, Q Star 1. Right? So last week, OpenAI released 01 preview and 01 mini. These are again, a new class of models.
Jordan Wilson [00:21:53]:
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 01 preview and 01 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.
Jordan Wilson [00:22:38]:
We are going to be seeing this probably on every single big piece of software. You're going to see agentic capabilities and they're good there's a good chance they might be powered by OpenAI. So So, hey, this is fresh off the press. Right? Couple hours ago, Sam Altman tweeted, incredible outperformance on goal 3 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. Alright.
Jordan Wilson [00:23:14]:
I'm gonna 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, sin 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 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 o o one 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. Alright. So we're gonna be talking, more about open AI here in a bit.
Jordan Wilson [00:24:25]:
Alright. And hey, live stream audience, if you didn't already get your question in, try to get it now. I'm gonna tackle them, try to tackle them at the end. Alright. 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 6 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. Alright.
Jordan Wilson [00:25:03]:
This is my list y'all. This is 6 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 and quarterly forecasts. Right? Now you're gonna hear the same thing with AI agent. So what the heck is an AI agent? And 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.
Jordan Wilson [00:25:45]:
But I'd say here are the 6 core functions that kind of constitute an AI agent. So you gotta check all of these box boxes. Alright. Not number 1 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 1. Right? If if 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.
Jordan Wilson [00:26:29]:
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. It needs to understand humans. Number 2. It needs to have tool interaction. It needs to be able to use outside tools. Alright. Number 3.
Jordan Wilson [00:26:47]:
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, o one model. Number 4, 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 I 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 2. Right? Tool interaction, but it needs that. Number 5.
Jordan Wilson [00:27:27]:
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. Alright. 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.
Jordan Wilson [00:28:04]:
There's other offerings that we'll be talking about here in a bit, from Google, Meta, etcetera. But it needs to actually be able to execute a task, which is one of the new things that we saw from a 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't. And then number 6. Learning and adaptation. Right? That's the other big one. If if an agent cannot learn and improve.
Jordan Wilson [00:28:38]:
Right? And normally, if if this is powered by a large language model, it can learn and improve, but it needs to do that. Alright. Let me recap those things quickly. 1, large language model and natural language, and natural language processing. 2, it needs to have tool interaction or outside tools. 3, the ability to plan or chain of thought reasoning. 4, memory and or access to company data. 5, the ability to actually execute tasks.
Jordan Wilson [00:29:02]:
And 6, the ability to to learn and adapt to become better. Alright. Those are the 6 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 open AI's o one model will do in the future. Because right now, o one does not have tool access. Right? If I'm being honest, if it had access to all the tools that GPT 4 o has access to, for example, code interpreter slash data, advanced data analysis, if it had access to, you know, DALL E, 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 o one if the o one 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.
Jordan Wilson [00:29:59]:
Although OpenAI said, that should be around the corner. So you know, essentially, you have the 2 different pieces with chat gbt and OpenAI right now. You have the GPT 4 model, which doesn't have, you know, number 3 on this kind of the ability to plan chain of action, chain of thought reasoning and tax tax execution number 5. But, you know, GPT 4 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 Sam Altman means a lot more than we think. Alright. 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.
Jordan Wilson [00:30:50]:
Alright. Let's go over a very very brief history. Very brief history. Alright. Because I I I don't want this to accidentally be an hour long podcast. Alright. You can't really talk about, modern day AI agents without first shouting out langchain. Alright.
Jordan Wilson [00:31:05]:
Langchain was very early to this game. So in October of 2022, langchain launch it launched. And you know, this was essentially, you know, very ahead of its time. Don't get me wrong. But think of this as a way it was kind of like duct taping in 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.
Jordan Wilson [00:31:38]:
Right? Oh no. Wait. That was 23. Sorry. So first, Langchain in Q3, introduced l c e l 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 GPTs, that was the ability for essentially agentic task.
Jordan Wilson [00:32:04]:
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 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. Alright. Then we can fast forward to early 2024. So NVIDIA, showcased their AI agent hardware acceleration.
Jordan Wilson [00:32:35]:
So you can't skip over NVIDIA's involvement in this. And then we go to, April 2024, Meta AI, with Llama 3, has started to integrate and slowly tease out, its agentic workflow. 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, 7 days, we've seen OpenAI announce the o one 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.
Jordan Wilson [00:33:36]:
Alright? But AI agents have been around for a very, very long time. Alright. 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 interchange work, how they could change work, it's like, yo. 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.
Jordan Wilson [00:34:05]:
Right? I already talked about Apple. Right? Apple with their Apple intelligence. So Apple and Microsoft, they control the the devices that we use. And Microsoft has already all out said, yes, AI agents. They're here. They're they're a big part of what we're doing. Apple is not there yet because they're like 2 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.
Jordan Wilson [00:34:31]:
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.
Jordan Wilson [00:34:54]:
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 1,000,000,000 of dollars into large language models. They have their own large language model platform. Amazon Q.
Jordan Wilson [00:35:23]:
They're working on simple agenda workflows, and then Meta as well. Right? Those are literally the 6th largest companies in the United States, and 6 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. Alright.
Jordan Wilson [00:35:58]:
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 a video on this. We'll leave that video in the newsletter. It was a short video, 5 minutes, that kind of shows, how this kind of no code or low code agent building works in their agent force.
Jordan Wilson [00:36:26]:
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 and the rules of how your AI agent can respond. And then what happens is it is connected to your live Salesforce data.
Jordan Wilson [00:36:53]:
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 and 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.
Jordan Wilson [00:37:43]:
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 2:30, and then that's one of the options. And then the, agent force agent schedules it and then updates the CRM accordingly.
Jordan Wilson [00:38:22]:
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 gotta take this with a big grain of salt, but then you have, similar offerings from, you know, other tools, softwares, like right.
Jordan Wilson [00:38:59]:
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 assistance, 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.
Jordan Wilson [00:39:27]:
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, 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.
Jordan Wilson [00:40:19]:
Coding is another one. Right? We're talking about real business use cases. I I know I asked you all. I got mixed responses. Right? Do you wanna 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? Etcetera. These tools like repllets agent, Cursor AI. Right? Being able to code with AI.
Jordan Wilson [00:40:51]:
Devon, you know, the AI software engineer, from Cognition. Right? Which OpenAI, prominently featured in its o one announcements. Right? So those 3 companies alone, there's a lot more, but these are probably 3, you know, Cursor AI, Replit agents, and and, Devon from Cognition are probably 3 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, etcetera, but then this also gives new capabilities. Right? That's the other thing with AI agents. They bring new capabilities to anyone else.
Jordan Wilson [00:41:35]:
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, 3 or 4 or 5 prompts, I actually have a piece of software that I created that solves one of my own problems. Right? 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 agents, Cursor AI and Devon are great examples of those. Benefits.
Jordan Wilson [00:42:15]:
Right here as we're wrapping up. You gotta be able to see the benefits. Right? This is 247 nonstop work. Right? If you're if we're talking more in the autonomous agent side. Salesforce itself said they see 1,000,000,000 in the next year. 1,000,000,000 with a b. Y'all, I'm not crazy. People think I'm crazy when I come off with these hot takes.
Jordan Wilson [00:42:37]:
There's gonna 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 1,000,000,000 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? Abso freaking lutely. Could it or sorry. Could it be a reality? Yes, hex.
Jordan Wilson [00:43:01]:
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. 2 or 3 years ago, you know, us dorks were sitting around on on Reddit and Quora being like, wouldn't this be cool? And now it's it's reality.
Jordan Wilson [00:43:30]:
Right? So the benefits, 247 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 in in in intelligent agents. You know, probably more in the manual or semi autonomous side.
Jordan Wilson [00:44:02]:
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 5 by now. That's also probably a key for me to go a little faster. Right? Alright. We can't talk about this without talking about the challenges and limitations. Alright? So the quality of reasoning is huge. Really defining this agent computer interface or ACI as it's sometime called, in designing, 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 is is huge, and we'll see, with Vertex AI agent builder from Google, how that goes.
Jordan Wilson [00:44:52]:
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 or sorry, learn and adapt. Right? And then as we start talking about multi agent environments. Again, I'm not a a a doomsayer out here talking every day about Skynet and Terminator, but you gotta think about that. Right? Hey.
Jordan Wilson [00:45:28]:
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 o one 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. Alright. 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.
Jordan Wilson [00:46:19]:
Right? So I don't wanna I don't wanna 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 1,000,000,000 of dollars into, generative AI, into GPU's, into AI agents. Right? Yet they're laying off tens of thousands of employees with record high profits.
Jordan Wilson [00:46:55]:
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 in 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. Alright. Then the future and what's next.
Jordan Wilson [00:47:24]:
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 6 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.
Jordan Wilson [00:48:05]:
The biggest companies that dictate how we work, all going all in on AI agents. Alright. 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 you know, you can't really talk about that shift without talking about AGI. And and you know, our, 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 can do all of these tasks without really much human intervention. Right? And I think agents are a step to get us there.
Jordan Wilson [00:48:48]:
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 gotta do a show on this. Right? I think a year ago was still too early, but now the writing is on the frigging wall, y'all. You gotta pay attention to it. Alright? And we have to talk about the duality. And we have to business leaders and decision makers.
Jordan Wilson [00:49:21]:
You have to do, AI agents and and kind of this implementation in the right way. You have to prioritize humans. You have to pry prioritize safety, guardrails, data, etcetera. You can't skip over this in the mad rush to be the 1st big company to implement something from agent force, to implement, you know, the 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 kinda screwed. Right? You should already be having that conversation about these autonomous workflows, AI powered automation and AI agents.
Jordan Wilson [00:50:00]:
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. Alright. So, let's wrap. Got a couple of questions here. Let's see. Let's see.
Jordan Wilson [00:50:19]:
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 livestream 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.
Jordan Wilson [00:50:52]:
He says, I'm curious about your thoughts Jordan on billing 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, 1 or 2 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.
Jordan Wilson [00:51:29]:
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 the 2, 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 you know, you are I won't say duct taping it. Right? It's a little more seamless when it is built in to the software that you're using on your computer, versus when you're having to kind of put the pieces together. Alright. Marie asking, devil's advocate here again. If AI is going to quote unquote takeover, what are humans going to be able to do? Good question.
Jordan Wilson [00:52:23]:
Right? Yeah. I think that's important to talk about, and I kind of, wrapped 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 1,000. 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.
Jordan Wilson [00:53:01]:
It's gonna 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, 4 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.
Jordan Wilson [00:53:32]:
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 and learn and change. Right? That domain experience you have up in your brain all of a sudden is much less valuable.
Jordan Wilson [00:54:18]:
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 Force 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 gonna wrap it. Tara asking, how can we ensure fairness in detecting AI agent use in the workplace and schools and prevent false accusations 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.
Jordan Wilson [00:55:16]:
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 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 gonna 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 gonna continue to tackle it here on everyday AI. Thank you for joining us.
Jordan Wilson [00:56:04]:
If you haven't already, please go to your everydayai.com. Sign up for the free daily newsletter. We're gonna 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 the same conversation at the same time next year. I wouldn't be surprised if there are 1,000,000,000 of AI agents out in the world. That wasn't just my bold prediction. You heard it from Salesforce, as well.
Jordan Wilson [00:56:26]:
But all I know is the future of work is generative AI, and the largest companies in the world are going all in on AI agents. Thanks for tuning in. We'll see you back tomorrow and every day for more everyday AI. Thanks y'all.