Everyday AI Podcast – An AI and ChatGPT Podcast - EP 398: How AI Agents Can Bridge the Gap to the Future of Enterprise Work

Episode Date: November 8, 2024

Why AI agents? And why now? You've prolly been seeing all the buzz around AI agents lately. Same. Here's the thing, though. There's more to them than meets the eye. Scott Beechuk joins us to dive in deep and tell ya what you need to know.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Scott questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Current State of AI Agents2. Challenges in AI to AI Interactions3. Guardrails in AI4. Humans’ Roles in AI Integration5. AI Agent Use Cases6. Future of AI AgentsTimestamps:00:00 AI agents are mainstream, bridging future enterprise work.05:28 Technological shifts drive innovation, advancing AI capabilities.07:40 Automate knowledge tasks and complex problem-solving cautiously.10:25 AI complexity requires new quality assurance strategies.15:53 AI agents optimize customer service interactions effectively.19:39 AI's future: Multimodal interactions with voice, video.23:51 AI enhances customer relationship building and sales effectiveness.26:57 AI development tools advancing, with complex AI interactions.29:13 Tracing AI interactions lacks standard communication protocols.31:41 Build companies by working backward for efficiency.Keywords:AI advancements, GitHub Copilot, Microsoft's WorkLab, AI guardrails, OpenAI, AI in sales, AI in customer engagement, Scott Beechuk, AI agents, machine learning, generative AI, ChatGPT, customer service, transparency in AI usage, automating customer outreach, multimodal future of AI, AI development, R&D in AI, AI systems' risks, Cursor, Zencoder, Replicant, Jordan Wilson, Norwest Venture Partners, Software development, Anthropic, Salesforce, EveryDay AI podcast, data privacy in AI, brand integrity with AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. About 18 months ago when I spent every day telling you all, hey, AI agents, you need to be prepared.

They're coming. They're coming. You guys probably thought I was a little bit nutty. But here we are now in the late fall of 2024. And AI agents are not coming. They're not some weird AI powered future. They are here. Right. So between actual large language model companies that have pushed this live, such as Anthropic Clawed with their computer use tool, and also all the names in Big Tech Microsoft, any day now rolling out their autonomous AI agents and co-pilot studio, Salesforce with their agent force offering.

AI agents aren't some weird figment of our future imaginations. They are not only here, but I think they are now becoming, stream and they are here to stay. So I'm extremely excited to talk about that today and how AI agents can bridge the gap to the future of enterprise work. All right, what's going on, y'all? My name is Jordan Wilson and welcome to Everyday AI. Before we get started, have to quickly shout out our partners from Microsoft. So why should you listen to the WorkLab 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 in your organization through your AI transformation.

That's W-O-R-K-L-A-B, no spaces available wherever you get your podcasts. All right. And technically, another place to get your podcast is Your Everyday AI.com, right? We have nearly 400 episodes, no matter what you care about, AI agents, marketing, sales, customer service. We have it all there, bringing the world's leading experts on the, the show to share their secrets with you and help us all prepare. So if that sounds like something you want to do, make sure you head over to your

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Enough to chat, y'all. I am excited to bring on our guests for today. talk AI agents and how they can really bridge the gap for the future of enterprise work. So please help me welcome to the show. We have Scott Bichuk, the partner at Northwest Venture Partners. Scott, thank you so much for joining the Everyday AI show. Hey, Jordan. Thanks for having me. All right. Hey, can you tell us a little bit about Northwest partners and what you all do there? Yeah. So Northwest, we've been around for over 60 years, global platform, venture and growth equity, invest across enterprise, consumer, and health care.

And we're here in North America, California, also in Israel and India. So tell us a little bit about kind of what caught your attention when it came to AI agents. You know, kind of like I talked about, they're not necessarily new, right? And I think they've been a part of this almost sci-fi storytelling for many times. decades. But from your vantage point at Northwest, when did you start really looking at AI agents and saying, okay, this is an area that we need to be invested in and paying attention to? Well, you know, it's funny when you mentioned sci-fi. I mean, Isaac Asimov used to write about this stuff in the 60s. And you're absolutely right. It's, you know, it's been a long time coming.

We've been investing in AI for over a decade. Machine learning, deep learning, transformers came about. And here we are in the generation of generative AI. I think AI assistance with ChatGPT and sort of the early versions of transformer-based GenAI were really exciting. I think we started to start to see some of the things that were possible. But here we are, I think, in the next major chapter of this unfolding. And AI agents now are more capable than ever. You said it earlier, and which is absolutely right, they're not coming. They're here.

and we are just on the precipice of unlocking a huge set of new capabilities for enterprises and consumers. Yeah, and I can understand because, you know, for the average person that maybe doesn't pay too close attention, things are happening fast, right? You try to keep up with what the big companies, you know, the Microsofts and the Googles and the open AIs are doing with their AI tools, their platforms. And now all of a sudden, you know, everyone is talking about agents. right? How do you think that this conversation has transitioned so quickly from, you know,

traditional AI and then we had the, you know, kind of quote unquote chat GPT or generative AI wave at the chat GPT moment in late 2022. And now everyone is talking about AI agents. Scott, why? Well, you know, I think whenever a major technological shift happens that could unlock a ton of like life-changing capabilities, you know, I think whenever a major technological shift happens, that could unlock a ton of like life-changing capabilities, you get the best minds in the world all piling on it. And so you're talking about millions and millions of developers all over the world that have really leaned into these big platforms. Start with Open AI and now you've anthropic and you have Cohere and Mistro and a whole variety

of open source projects that are pretty interesting. And when you put that many smart people on a single platform and a single architecture, you start to unlock the art of the possible, things happen really, really rapidly. I think it also helps that a lot of the larger LLM, third-party providers, they're starting to provide some pretty robust frameworks that we can work with to actually develop agents. I mean, Open AI alone has this amazing evolution that they keep unlocking more and more and more for developers.

And so, you know, you don't have to squint too hard to see a future where we start to build software that completely changes all aspects of how we live our lives and do our work. And AI agents takes it to a whole new level because now developers can start to not only build simple applications, but now we can actually build code that integrates with other systems, can draw on data sets, can learn, can fine-tune, and now can actually take action on other external systems,

enterprise systems, consumer systems, systems of action and data. And I think that in and of itself almost, is like a huge universe of possibility that we're just about to see come online. I think everyone rushes to the pros of AI agents, but with promise, there's also the peril. There's potential downsides. Scott, can you quickly just walk us through and maybe just talk about what are those ups and in the pros and then the cons? Because I think people just rush to what is possible or what could be possible. But you got to have guardrails. You got to think about safety. Yeah, I think that's, I think that's an important question because you start to think about

what types of human knowledge tasks we can automate with AI agents. And certain things come to mind that are that are somewhat obvious, like tier one customer support. You know, you got, you got tens of thousands, hundreds of thousands of people all over the world answering the phone, answering the same question every single day. How do I reset my password? How do I get access to my account? Well, those types of things, you can wrap your head around that pretty easily. But then you start to move up the stack and you start to think about, well, how do I automate more complex thinking? Things like selling or you move into some regulated industries like healthcare.

And how do we start to predict clinical types of applications that AI could handle? And you start to think about all the things that could go wrong. Right? So, I mean, if an AI agent gives a customer the slightly wrong information for how to reset their password, well, maybe not the end of the world. But I start giving, let's just say, I call up my insurance company and I'm stranded on the side of the road in a blizzard. And I need a tow truck. And that AI agent isn't getting me that tow truck ASAP. I got to stand out there for an hour.

I could, you know, that's a little more uncomfortable. And then you take it one step further and you think about calling up an AI agent on the phone or chatting with one online asking for, you know, some medical advice. And it starts to hallucinate and it starts to give you bad medical advice. Well, that's a whole other level of risk. So the more sophisticated than the more we move up the tiers of knowledge automation, the more important it is for us to think about how to build the proper systems, of guardrails and just accuracy, observability into these types of systems. Speaking of tiers and levels, I think this also helps frame why today's conversation is especially timely, right? About two or three weeks ago, OpenAI CEO Sam Altman kind of

acknowledged, you know, they had released or, you know, leaked out their kind of five levels to HEI, right? Level one was chatbots, level two reasoners, and level three agents. And he kind of said, hey, we've achieved level two now with reasoners, right, with this new 01 reasoning model. And then he said, once that happens, the agent's stage comes pretty quickly. It's not as big of a gap between steps ones and two as it will be from steps two to three. You know, when we think about Scott, AI agents being able to reason with other AI agents, what does that unlock for enterprise? Well, it can unlock a lot of good things, and it can also unlock a lot of the need for a whole new way of looking at quality, quality assurance across the enterprise.

Because you can imagine a single AI agent reasoning with a single human being, okay, we can understand if it were to hallucinate or were to get something wrong, easy to course correct, because we can sometimes, you know, just simply go back and change the prompt or the query that we send it. But imagine a world where AI agents are talking to other AI agents and they're hallucinating a little bit every step of the way. Let's say we're stringing together 10 of these things. And as a human, I submit a request to a network of agents that are orchestrating together and reasoning with one another in order to achieve a more complex task. Well, the problem there is that it's like the telephone game when we were kids. You know, you whisper into somebody's year, one concept, and then that kid whispers into the next year and the next year. By the time you get to the end of the 10th kid, you know, whatever the original person said completely different. Well, the same thing could happen in a network of agents, but in a far more profound way, right?

Because these agents could be solving something that goes completely off the guardrails and completely, you know, produces something that not only is incorrect, but might not even be relevant. So these systems can have this exponentially a steep error curve. And so the idea of being able to trace the accuracy of the agent network as they communicate with one another is something that's new and something that some companies in that category are starting to try to figure out in the future. how do we actually trace AI agent networks, the way that we trace complex service-oriented architecture in classic software? I think that's an important one, and I want to get back to this zoomed-out overview,

but maybe if we can, Scott, let's maybe zoom in here quickly and talk about some AI agent use cases, right? Because I could go all day on the theoretical and I want to get back there, but maybe it will help our audience along a little bit. If we can talk about actual use cases for AI agents, and I think one of the, not saying lowest hanging fruit, but one of the easiest ones to kind of walk along that journey with and really understand is in customer service, right?

So as I understand, Northwest has an investment in a company, replicant, you know, working in the customer service space. And I think it's one of the areas most ripe for disruption from AI agents, personally. But can you walk us through kind of like what that use case looks like and maybe tell us a little bit about Replicant? Yeah. So Repliquant was a company that was started before November 22. So before we had access to things like ChatGPT. And what they do is they act as a Tier 1 support agent on the phone. So if you call up AAA insurance in North America, there's a pretty good chance you're going to get Replicant and Replicant AI customer support rep on the phone. And they are trained.

by millions of calls and millions of interactions with customers. So hopefully they become as good or even better than your best support rep that you might have otherwise had. But Replicant today is a different company than they were back in 2019 because now we've got generative AI. We can make the conversations even better, more robust, and more accurate. And they can also do more complex things. So you can imagine a scenario where I'm on the side of the road

and I need to call, you know, a tow truck. We were mentioning that earlier. Well, that's a scenario that involves a lot of integrations to other systems because I'm calling up. I need to figure out that it needs to know where I am. So hopefully we can use GPS. We can tell it exactly what happened to the car, what type of tow truck service I might need, where I need to go.

And then it can integrate and it can actually make outbound calls to the tow truck services. And that is a true. That is what we call true AI agent integration and action. And a little bit more complex use case than we've seen in the past. But that's what's happening today. And that is like AI agent 1.0. There's going to be far more complex use cases coming up. Yeah.

Oh, absolutely. And I think that's a very good use case to talk about and walk through because it's probably beneficial for everyone, right? Kind of like what you said, probably a lot of these customer service humans are answering the same questions probably over and over. And then the humans on the other end are probably waiting in a long line. And there's probably some disconnect there along the way. But going back to this kind of two sides of the coin, because I mean, you can immediately see how that is huge. Right.

In theory, you know, AI agents can understand human language. in the same way that a human can. And they can in like sometimes more accurately also route a customer's query, right? Because they have a company's entire, you know, knowledge base essentially in their training data where if you're, you know, talking with a brand new customer service human, they might not really know how to, you know, handle more complex queries. But then can we go back into the guardrails a little bit, right? because going from generative AI and being able to work with unstructured data, the promise is huge.

But even for when we're not talking about, you know, multi-agent environments, how important are those guardrails for enterprise companies? Because I assume that's going to be one of the biggest hangups for enterprises in 2025, not adopting early. I agree. I think guardrails, when we talk about guardrails and AI agents, I think a lot of people just to assume it's just about hallucinations, or it's just about getting something slightly inaccurate, which LLMs tend to do, right? These are non-deterministic systems. What they output doesn't always agree with the query that you thought you intended to ask. The output sometimes is a little unpredictable. But it actually goes beyond just inaccuracy. For some companies, it actually can become a brand issue. And so, you know, how do you, how does your organization view a certain

you know, ethics issues. How do you talk about certain political issues? How do you think about culture and how do you want your customers to view the sort of the empathy or the sympathy of your particular company or your brand? And those things are a little softer, but they also matter. And so guardrails are really designed to solve for all of those types of challenges to make sure that agents represent an enterprise the same way that you're, your best, most sort of enabled, compliant employees would do whenever they interact with your customers. Yeah. I think that's a great point.

And also, the capabilities, right, of these agents are changing quickly, right? Like you kind of already referenced, you know, a lot of these companies existed pre-generative AI. so their capabilities were much less robust, right? It was much more, you know, binary zero and ones deterministic. And now the capabilities are, in theory, quite limitless. So, you know, as we look into the future, which I know it's hard to do, I'm not going to ask you to bust out your crystal ball, but for decision makers right now at enterprise companies,

what are some of the most important things that they need to consider, right? Like what you just said with the brand issue. like how AI agents can actually be a brand issue is a huge and I think an important callout. But what else should enterprise leaders making decisions on should we go all in on AI agents? What else do they need to consider? Well, one interesting thing that's coming that I don't think a lot of enterprises have fully groked yet is the idea that the future of AI agents is multimodal. And so we've been sort of accustomed for the last couple of years to chatting with our

AI assistance and now AI agents and chatting, you know, using text or using voice in some cases. And but but where we're going is you are going to interact with, we are all going to interact with AI agents via voice with video. We're going to be able to send and receive images. We're going to be eventually we're going to be wearing, you know, AR glasses that I think will become ubiquitous before we know it. And when we have systems like this to interact with agents that are more than just the chat GPT text window, I think that changes all kinds of things about the type of service that we can deliver over AI agents and the types of concerns that enterprises are going to need

to have about how to govern those systems and then thinking outside the box, because if you want to be a competitive enterprise in the future, you're going to have to, embrace and support these multimodalities of interacting with AI agents, just the same way that we did, look, when we're chatting over video right now in different cities in the U.S., we think of that as second nature now. Well, in the near future, we're going to think of it as second nature to have these multimodal experiences in AR, VR, VR, video, real time. We are going to have devices that we carry with us that are starting to come online now, like meta's, you know, Rayban glasses that are taking video and taking audio. And then you've got different devices that we wear now that have all

kinds of sensors on them. And all of this sensor data, all of this intake will be available to AI agents. And so we're also, enterprise are also going to have to think about privacy. Data privacy has been on everyone's mind for the last couple of decades, but it's meaning, the meaning of what we do as enterprises with our customers data and how we allow them to control the use of that data in exchange for some value back is going to take a whole new meaning. Yeah, I think even the thing with meta, right, seems simple enough, right? Okay, you know, you have some some ARVR type glasses that interact with, you know, Lama that's, but then you also see some new advancements, right?

like meta previewed the Orion version of those glasses and much more capable, right? So it's not just about, you know, computer vision and interacting with a large language model. I think it's much more than that. But, you know, I'm wondering, Scott, though, since your company, you know, at Northwest, you invest in multiple companies that are in the ancient space, could you give us a little bit behind the scenes of maybe what you see coming next? And a great example there is it's no longer just taxed. It's multimodal.

It's AR, you know, it's AR, maybe VR, right? But where are these companies kind of shifting and setting their sites on? Because ultimately, that's going to give us indicators as business leaders where we should be focusing on as well. Yeah. Another big area, and I'm sure you've been following this. I know a lot of your viewers are probably following it is the area of sales and engagement with customers. We talked a little bit about customer service, but I think it's very different. When you're selling a product, let's just talk about a complex product to customers.

You know, there's a lot of different, again, tiers of how we do, we engage with customers and we sell. Sometimes that initial tier, the initial outreach tier, we call the SDR, the sales development rep. Well, there's a lot of companies right now that are starting to try to figure out how do we automate that SDR tier. And that's, again, you can wrap your head around that because it's kind of like a tier one support agent where, you know, the outbound is, or the inbound intake is kind of, you can almost like it's a bounded set of tasks. But then you go up one tier into the account executive tier where someone actually has to build a relationship with a customer and you have to have a long running set of interactions with somebody and build trust. Now, that is a way more interesting problem to solve with AI for me. I think we're going to go through two phases. Oftentimes, AI agents go through the phase of human in the loop,

where an AI agent is actually supercharging a human being. And if you're a count executive trying to build trust with a customer, and you're trying to educate them on your product and your company and help them fall in love with your brand, And, you know, AI agents now can, you know, listen in on those sales calls. We have, for example, one of the companies that we invested in very early on was a company called Gong. And again, before the latest Gen AI revolution, but even today, Gong is more relevant than ever because they're teaching account executives in real time how to sell better, how to be more effective, and how to make better use of every customer's time. But you can imagine where that all can go.

Because in the future, some companies are going to adopt AI agents that do the selling and actually conduct the demos and actually facilitate multiple meetings over time with customers and build trust with customers as an AI to human interaction. And that's an area I think everybody should keep an eye on. I have a question or two to follow up on that. real quick, have to give another quick shout out to our partners from Microsoft. So why should you listen to the Work Lab podcast from Microsoft? It explores the questions business leaders are asking.

How can they guide their organization on their AI adoption journeys? How can the technology help them create new products and business models and maximize value? How should they help their teams reskilled for this new area of work? And why is it important to be completely transparent about when and how you utilize AI? So find those answers on WorkLab. That's W-O-R-K-A-L-A-B-N-S-A-B-N-S-A-B-No spaces available wherever you get your podcast. All right, so real quick to follow up on that last point that you made, Scott. So as an example, you know, a Tier 1 SDR, you know,

oh, that could be an autonomous AI agent and doing the demo and answering questions. So where are in the short term, right? I can see humans really kind of just overseeing, right, and setting these up and putting those guardrails and more data, but, you know, past that. Where are the human roles going to shift in change? And how is the kind of quote unquote human work going to change once we have more capable and more robust AI agents?

Yeah. Well, I mean, one area that we could talk about is R&D because the idea of building software is something I think will persist at least for our lifetimes. But the way that software is built is changing really rapidly. Today, we have great products in the market like Microsoft's GitHub co-pilot for helping writing code, and you've got a lot of other companies out there like Cursor and others, a new company called Zen Coder that's also playing in the game. And these are companies that are helping junior developers and sometimes even more senior developers really accelerate the ability to get code into production. But by doing all of this and building AI agents in this new way, it introduces a new conundrum.

And the conundrum is what happens when AI agents are talking to other AI agents. And we're stringing together a network, a web of AI agents that are solving far more complex problems. In other words, I ask my AI agent system or my product to help solve a big problem for me. hey, I want to, as a, as a, you know, in venture capital, I want to invest in a category. I want to find what is the best company in this new world of AI agents that's solving the marketing problems of the world. Well, that's probably a problem that a single AI agent alone wouldn't be able to help me solve. It's probably one AI agent talking to another AI agent. So one's doing the reasoning.

They're trying to figure out, okay, what is the domain that we're really talking about? is the size of that universe. Then another one goes out and starts to do research on a lot of the companies, the startups, but they can't find them all. So they go to, it talks to another AI agent and says, here are the companies that I found. Go find all of the private companies or the stealth companies that we don't even see online right now. So that's all these strings of different reasoning abilities. But the problem is that AI agents, like human beings, are inherently inaccurate.

They're not perfect. They're, again, non-deterministic systems. So in classic software development, if I'm an engineer, I will use a tracing ability. I'll be able to trace if I'm making different services, different classes, different functions, maybe it's a serverless architecture. I am actually following from API to API call exactly what's going on. The bits and bytes that go in equal certain number of bits and bytes that go out, and I can observe and I can monitor the accuracy of all of those interactions.

And those are if we find inaccuracies, we call them bugs, and we set somebody to go solve it. But in the world of AI agents network together, that takes a whole new meaning. Because what is an AI agent to AI agent interaction? Well, there's no API for those to talk together. Today, they may talk to one another using JSON or some other, you know, sort of technical structure. But they might not. They might actually use human language. Or who knows, we've seen experiments where AI agents talking to one another invent their own language to talk to one another just because it's more efficient.

So how do you, as a software developer, observe the interaction between AI agents in a system like that and figure out, okay, well, there's a 1% inaccuracy between the first two steps, a 2% inaccuracy in the next two steps, and so on. And by the time the whole system is ready to present its results, the thing is like gone way off the guardrails and you're in another universe. It's not, you know, the inaccuracy has gone, has compounded throughout the system. So we're going to need to develop more robust systems for monitoring, checking for accuracy, observing all these things, and then ultimately fine tuning and putting the right guardrails and iterate recursively back to fix. the prompts into each one of those notes. I love it. I could dork for hours just about the fact that, right, these AI agents are creating their own language to talk with each other because maybe English is not efficient enough. But Scott, we've covered a lot in today's episode from, you know, kind of the history of AI agents, pregenerative AI. We've talked about now some use cases,

some potential problems and the promise. But as we wrap up, up, what is the one most important takeaway that you think people need to understand when it comes to AI agents and how they can kind of bridge the gap to the future of work in enterprise? Well, you know, I think what we need to do is we need to think about working backwards as we build companies. So I'm going to take the perspective of the founder, the world of founders who are building startups. And so what the opportunity, is to make life far more productive for all of us humans here, make it way more efficient for us to get things done. And that's what technology has always done. But oftentimes, we make the mistake

of working forwards instead of backwards from what we want. If we work forwards and we say, well, somebody dropped this cool, you know, LLM on me, let's go see what we can do with it. That's a great academic experiment. But those, taking that path doesn't always lead us to the best conclusions. What I think we all need to do is we need to really think carefully about our own businesses and say, what are the things in our business that we would love to solve, that we would love to automate? Maybe it's in the front office. Maybe it's a back office, a finance type of problem. And then if we work backwards from the goals that we have, we will have clear business cases.

And then we can go out and either build those systems, you know, with knowing the intended result, or we can seek out solutions or partner with other companies because I've never seen so many new companies getting formed right now, especially around the area of AI agents. So there's going to be a lot of incredible technology and incredible products coming out. And I think a lot of those companies are going to need to work together so that we can actually orchestrate AI agents from one company to another to solve our more complex tasks over time. It is definitely a hot space, and it is moving very quickly. But Scott, I think you helped us all understand it a lot better.

So thank you very much for your time and sharing your expertise on the Everyday AI show. We really appreciate it. Thank you, Jordan. I really appreciate it, too. All right. As a reminder, y'all, we covered a lot there. I know that the AI agent space and how quickly it's moving can be very confusing. So don't worry.

We're going to be breaking it. it all down, all of the best insights from today's episode and a lot more on today's newsletter. So make sure you check that out. If this was helpful, don't be greedy. Share this with a friend because we all need to understand where this, you know, AI agents entity is heading because it's fast. Speaking of fast, you're going to learn fast if you go to your everyday AI.com. So thank you for tuning in today.

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