The AI Daily Brief: Artificial Intelligence News and Analysis - The Agent Use Cases Most Ready for Primetime

Episode Date: April 18, 2025

In this discussion, AI strategist Nufar Gaspar explains the top AI agent use cases companies currently use successfully. Examples include customer service automation, coding assistants, sales agents, voice-enabled workflows, and deep research.Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠⁠⁠Plumb - The Automation Platform for AI Experts - ⁠⁠⁠⁠⁠⁠⁠https://useplumb.com/nlw⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown

Today on the AI Daily Brief, the most in-demand agent use cases right now. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends. Spring break week continues with another interview episode, and this time I am once again joined by Newfar Gaspar to discuss the agent use cases that we're seeing come up most often. Newfar is once again a brilliant AI analyst product and strategy leader who has consulted with some of the biggest companies in the world on AI strategy, and who works with us at super intelligent on our agent readiness audits and our agent marketplace.

Today, we're looking at some of the agent use cases that are most in demand coming out of both our audits and our agent marketplace as a way to potentially help you understand what agent uses are actually ready for prime time and which still remain a little bit farther away. All right, Newfar, welcome back to the show. This is not exactly a part two from what we did before, but I think a lot of people will we'll connect the dots. Before we were talking about what mistakes are common that we're commonly seeing among organizations as relates to AI broadly, but agents specifically. Today we're talking

about what agent use cases are actually ready for prime time, what agent use cases are being implemented right now and people are finding success with. And again, this comes back and harkens to sort of a theme from that previous show as well, which is, you know, how can we help organizations spend their time more effectively in this agent and AI transformation? So let's, let's, dive in. We're going to talk about some use cases, specifically, obviously. But I think that where we wanted to start is talking about just based on our set of experiences, which includes, you know, a huge number of conversations with different types of companies, both in the context of the super intelligent work with these agent readiness audits, but also in terms of your independent

practice and consulting. What are we seeing on average, at the risk of maybe being overly reductive? Where are orgs with agents on average, at least the enfranchised organizations that we're tending to deal with. Yeah. So let's assume that there is some bias because if they're very, very mature, perhaps they will not come to us. But everything that we've seen thus far are companies that we categorize as either agent initiation or agent exploration phase, meaning that they are either just starting to contemplate agents or maybe they've started working on a handful of agents or some agents' ideas. But in general, they are very early stages. And like we talked about the seven common mistakes, in many cases they are so not ready that they have a lot of work to do in preparation for agents

even before they can introduce the first one. So they're very, very early on. We are seeing some organizations and we actually encourage them in some cases that almost have no AI adoption but are looking into agents as the way for them to bridge the gap of how much behind they are. In some cases, these are smaller organizations where it makes more sense for them to hire an agent versus hire a user. an employee. And in other cases, they will still have to do the groundwork of getting agent ready

before they will be able to do this bridging the gap with agents. Yeah, they want to talk about also the advance. Absolutely. Yeah. So I think maybe maybe a better way to frame this even than just where orgs are on average is sort of what's the band of organizations that we're seeing, the common band from, you know, beginner to sort of a little bit more advanced. Yeah. Yeah. So from what we've seen, even the most advanced organizations, one that already have agents in production, we're only talking about, in many cases, some handful of meaningful agents in production. And I'm not talking about the personal productivity that individuals are creating for themselves. These are not in my book meaningful agents in production.

And I'm not also going to discuss the question of whether they're custom GPT or other such assistance or agents. Let's negate them from the discussion. they do have in production, in many cases, off-the-shelf kind of agents that they built with vendors like Agent Force, Microsoft and others. The other cases that we're seeing, and we'll talk more about the actual use cases, but of course, customer support is by far the most predominant category of where agents are actually mature in production, and a handful in some cases of other supporting agents in some very well-focused functions within the company. So I think that that bridges us into maybe where organizations are not. So the second thing that we wanted to explore is, again, set up to what agents are ready for prime time. There are some pretty distinct patterns in what we're not seeing. I was on a conversation with a group of chief AI officers a couple of weeks ago at this point, you know, a number of from the finance industry, some from pharmaceuticals, a pretty big range of different companies. And there was one thing that was really clear. And these are definitely. more advanced, or at least these are the most advanced portions of their organizations. Maybe the

organization as a whole is in advance, but these are people who are highly engaged, highly enfranchised, really thinking about these things all the time. They're the internal champions. And it's very clear that where they want to get eventually is agents involved in core business functioning, right? If they are in insurance, they want agents to actually be making decisions beyond just sort of providing, you know, algorithmic advice like, you know, predictive analytics have done. They want to reorganize their whole companies around, around agentic capacities. However, to a person, none of them really feel like agents have the complexity, sophistication, duration capability to be used for those specific purpose-built use cases that are the very,

very core to their company. And so instead, it seems like the place that they're going. is focusing on not things that are unimportant, but just other parts of the functioning of the company that are, call it lower risk, right, that still allow them to get used to integrating agents into workflows, but are on things like customer service, marketing, sales. Is that something that you're seeing as well? Yes, but I'm not sure about the observation that the technology is not ready for what they have in mind.

I think in many cases, the organization is not ready, whether it's from culture, technology, skills, use cases. In some cases, they don't want to tackle the most contradictory thing that they can do to get their employees basically to create an uproar because they're seeing the future. So that might be another thing. There is also a fear to take your core business and offload it to AI because of the potential pitfalls beyond the employees. And then lastly, technology perhaps is not ready in some cases, but I'm not sure whether in all cases this is indeed the case that the technology is the biggest hurdle. Sure. So basically it sounds like you're seeing the same sort of inclination towards, you know, orthogonal use cases rather than, you know, core business

function use cases. You're just, you're not sure that that's more technology or the organization itself or some combination thereof. Yeah. And in many cases, we're saying that other people are doing what other people are doing. So there is like a momentary. here of automating the support function first. I think from a business perspective, it makes sense to deep your fit in the water where it's more safe from various perspectives and then go there. Just yesterday we had a conversation with a company that is very bold in their agentic approach, but they're saying let's get the efficiency first off the table and put all

of these agents that can free some of the bandwidths of our employees. And then let's tackle the core business with agents, not because we think that we can, because we want to have our employees, have more bandwidth to think about those core agents before we dive head deep with those. Yep. No, it totally makes sense. So, okay, then let's move to the meat of this conversation, which is what things people are doing right now?

What are we seeing, you know, most commonly in terms of the agentic use cases that are being deployed, that are ready for production, that are actually yielding results for companies? Yeah, so I can name a few and then add whatever I'm. missing. But one thing that is very straightforward and easy to use is basically to use agents that others have built, whether those are agents for coding that are probably one of the most mature. I think literally every coding platform now offer an agent. Some of them are better than others. Some of them are agent native versus others that are just introducing agent almost as an afterthought, but those are getting some good momentum and some positive feedback. And of course,

my personal favorite deep research agents that are doing amazing job, and we're also seeing some companies basically creating their own version of deep research so that it can work internally or in their own terms, and these are some very good use cases. The other is probably simplest thing that people are doing is augmenting the classical like Zapier or make automations with more agentic capabilities, whether it's for planning, some open-ended tasks that currently agents can do in beforehand they couldn't, or augmenting them with more like an LP-based interactions of text and speech, but just adding them to the existing flows concretely or metaphorically by creating similar automations using other tools.

Let's actually pause. I want to break these apart a little bit because there's a lot to dig into here. So let's talk about the augmented automation a little bit because they're frankly the least interesting of these things to me. There's sort of the, there are a very obvious starting point, but they are, this is the area where people love to debate, are these things really agents or not? Like, you know, what should we call automations? What should we call agents? I'm well on the record with this one that I think people should give up the ghost and agents are close enough and actually people's understanding of that term is directly correct and they should just be fine with it. But it feels like this is an area where there are certain types of tasks

that are just, they're so begging to be automated more. And it's, it's a very important. And it's, It's really just figuring out these sort of very slight customization improvements for existing attempts at automation that are, it feels likely that these things are going to be completely boring, rote, and normalized, you know, inside of a very short period of time. Galileo called these the digital assembly line in KPMG's taco framework. They called these the taskers, right? Things that are very, very specific. I mean, how much are, how much are organizations getting fired up about these things versus

they're already in the kind of table stakes column. Yeah, in most cases, I think those will be like stuff that the employees will individually create for themselves and thereby they will not move any needle. In some cases, though, you are seeing some business processes that even using this method can be automated significantly more than what they've been doing thus far. And in those cases, you might be able to see even a higher use cases

that are implemented using a not very complicated technology. So basically, there's a risk at undervaluing the simplicity just because it is simple in terms of its potential business impact. Exactly. Yeah. Let's talk about deep research and coding for a second. And because for my money, I think that these might be the two agentic augmentations, however, you know, agent categories that to me it is very hard now to justify doing things. that you used to do without them, without them today. I think that the capabilities of research tools are, it's very hard for me to see how people who, I don't do a single thing that involves any

sort of research without using these tools at this point, right? And in general, my workflow often involves cross-checking two to three to four of these to see how they come up with things, right? I have, you know, three different versions of deep research running at any given time. Now, obviously, we're just scratching the surface of deep research possibilities because the versions that we're using are sort of very, you know, individually designed agents that don't have access to proprietary knowledge bases or, you know, and obviously what enterprises are thinking about is how to plug those into other data sources. But it feels completely like not, not in six months, not in 12 months right now. If you are doing anything that involves sort of research or strategy and not using those tools, I tend to think you're behind. And I think that we're pretty pretty much in a similar spot when it comes to. coding. Now, coding is interesting because there is, I think, an ironic or surprising, at least, amount of intransigence when it comes to adoption of the sort of coding agent tools and vibe coding tools and things like that among enterprise developers. And to some extent, there are, there are pieces of it that are understandable, right? Like the first generation of these tools that

are becoming popular, right? The IDES, a cursor in windsurf, the specific text to code tools like Bolt and Lovable. They're absolutely optimized right now for an individual sort of developer experience, as opposed to integrating and interacting with these massive legacy code bases that have thousands of people working on them and, you know, a guy who might, you know, be typing on them the next day someone different is using that code. But it still feels basically criminal at this point to not be taking advantage of these sort of new efficiencies of these coding tools. I mean, we had super have, we had to let developers go who wouldn't get with a picture, basically, to change their processes around them. You know, is there, at what point do organizations just start to mandate that these

are now the way that you do things is, you know, if you are not agentically augmented in these categories, you're just out, you're too far behind. Yeah, you know, you're very savvy in that, but when we talk to organizations, there are so much behind you. And, lot of that comes from what you're saying that it's very optimized for the individual user, but another part of it, which is probably the lowest hanging for it, they just don't know the breads of possibilities that these tools can take you. Because deep research, for example, people get stuck on the deep research part and forget that it's just like a deep reasoning agent that can literally get you any complex task done much better than any other AI tool. So they,

they don't understand that. Similarly with coding, like when I'm talking to engineers of everywhere are on the stack. Low level, hardware, high level, in many cases, they just didn't spend enough time to understand all the use cases that they can do with them. And that's why they're not using. So I agree with you that they should, but they need to spend some more time to get comfortable with the existing tools. And then they can augment them and modify them to be like enterprise ready for their needs. Today's episode is brought to you by Plum. If you're building agentic workflows for clients or colleagues, it's time to take another look at Plum. Plum is where AI experts create, deploy,

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success with its clients at KPMG.org.us slash AI. Again, that's www.kpmg.comg. us slash AI. Now, back to the show. I do think that you're right that. So with deep research, the terminology, the name actually, even though it's sort of been universally adopted, right? It's called deep research for both open AI and Gemini. It's called deep search for GROC. It potentially distracts people a little bit from the full set of possible use cases by being called research, right? I also think that by virtue of being embedded in these other tools, as opposed to a standalone thing that was introduced as a breakout kind of standalone thing, it's perhaps being underappreciated in terms of just how differentiated it is.

We at Super Intelligent have been among the companies and I've talked to lots of people who have had this experience who spend a bunch of time trying to build our own kind of system to wire a bunch of things together, only to just see if we could run it through deep research and have it produce way better results. And we need some like 90 letter German word for just do it with deep research and instead of trying to build it yourself. But I think that there is still, we're just scratching the surface of those use cases and it's going to take some amount of time of diffusion of people sharing their specific uses of deep research for it to fully embrace. You know, on the coding side,

I think that you're right. I think that it's going to change rapidly. I think that, you know, what I'm seeing is you're starting to see more discussion even within the enterprise around what things these tools can be used for right now. Right. So you don't want to use it as your sort of primary coding environment, but you should deploy these things for refactoring or whatever it is, right? You're starting to be more discreet about it. You're also seeing just an absolute flood of companies race in to try to fill the current gaps in capability and new challenges that these tools are arising from. So you're seeing on the consumer side, you're seeing companies that are coming in to try to make it easier to go from, okay, I've got this code base that I don't understand.

and how do I actually, you know, make it live on the internet and do things. There are companies that are coming in and doing that. On the enterprise side, you're absolutely seeing companies that are trying to come in and start to maximize for those enterprise use cases, even though they're more complex. So I think that that's going to change pretty quickly. Anything else on those before we move to sort of like the big 800-pound gorilla in terms of, you know, capabilities or things that people are doing now with customer support? The only thing that we need for super is to have the deep research on API.

So if someone in the decision-making process can create a very good APIable deep research, the better. Yeah. Dear OpenAI, I know some of you guys are listening, please let me know when the API is coming. All right. So let's talk about customer employee support, I think probably the area that is most discussed when it comes to agents. Yeah, so we talked about it before that customer support is probably the most mature agentic use case out there. but there is an abundance of flavors for customer support, right? We're talking all the way from a simple, very like a FAQ kind of an agent, all the way to the very impressive, completely autonomous, end-to-end customer support agents.

We're also talking about other flavors of that, that can be agents that are helping to upsell or cross-sell your product because they identify opportunities. So we're starting to see these implementations. we're also seeing similar notions in internal employee support, whether it's IT support, HR support, legal support, payroll, basically everything that requires someone to answer questions in various capacities, these are perfect agents.

And you can even kind of extend those to other types of support in the broad sense of the term, whether it's to help with employee learning and development or on board new employees. In many cases, these are the most prime time ready agents. that we have out there. And lastly, and you will probably claim that that's a category on its own, is everything related to outbound communication using various voice agents to create more sales or maybe reach out to candidates that we want to hire and so on. Yeah, I do think that those are, I think there's a couple different distinct categories

there. I think that the part of the reason, though, that you might want to connect them is that all of these have a common thread of talking. in air quotes, to a person or finding out some information about them or from them, and then integrating that with some pre-existing set of information, right? And this is just the, all the versions of that AI is really good at right now, right? And so you're seeing this Cambrian explosion of basically every type of that interaction that AI can do. So let's talk about voice agents for a it. I think that part of why voice agents are such a hot category is that this is a capability

that is really useful right now. I mean, this is something that we observe. Part of where our voice agent interviewing came from was observing that in other areas, voice agents actually were doing a pretty good job, right? So I was looking over at the hiring space where companies were already deploying voice agents to do, you know, initial screening interviews and things like that. And they were working pretty well. The voice capabilities are good. Advanced voice mode had come out. So latency was better. You know, all of these things had kind of come online as capabilities. And, you know, we had the thought, well, maybe you could basically turn that into a consultant whose job is just to sort of ask the right questions and grab a bunch of information. And it turns out that, you know, a hundred other startups or a thousand other startups are basically going through that same process of thinking through every other version of asking people questions. Right. So you have voice agents for for market research. You have voice agents for, you name it, right now they're coming out. And so I think that it's hard to call voice agents aren't so much a category as much as sort of a common underlying technology capability that begets lots of categories. But I think that as companies are thinking about where they

could be getting value from agents right now, it is not unreasonable to ask what are current functions that involve us talking to people, you know, literally talking to people. And would any of them be well suited for, you know, one of the copious number of voice agents that are out there, or, you know, rolling our own version of that. And there is so much value if you combine the voice agent with a deep research or deep analysis agents, then you get even a full-blown consultant that is autonomous, basically. I mean, that's, we call the sort of underlying technology behind, or at least I call it, I don't know if you'd call it this, but I call the underlying that we use for the agent

readiness audit, an agent consultant engine, because that's what you're going to be. it feels like, right? Its job is to ask the right question, obviously with us, you know, helping kind of give it some initial ideas about what the right questions are, and then, you know, do come up with and do analysis on the basis of some particular goals and particular knowledge, which, you know, starts to look very proximate to what consultants do. I think it's worth mentioning briefly, too, before we sort of broaden out again, the sales agent use case. This is one to me that feels very much, again, like, I don't know that I've ever run across, or, you know, at least in the last six months, run across any sort of sales organization that couldn't take advantage of the sales agent, you know, the sales type SDR type agents that are available right now. Now, that's not to say that they are, you can just grab one off the shelf and it's instantly good to go.

There is more work than I think people might imagine or might want when it comes to getting their, you know, SDR agents up and running. However, sales is, sales is an area where there is no risk, I don't believe, whatsoever of there's always more leads. You always want more potentials. You always, you know, if a sales agent, like a human sales agent or sales representative, had access to an agent that could get them 10,000 times the number of leads, they would be nothing but thrilled because ultimately more, more, more is the goal. And so I think that one, from an internal change management perspective. Sales is a really good area where it seems highly unlikely to me that we're going to see big cuts in the sales organization because of agents. We're

going to be straight, not in efficiency AI, but opportunity AI, where it's just how much more can we do, how much faster can we grow, how much bigger can we get? And I think that that's going to be a very useful bridge as, or employees try to figure out what management's goals are as it relates to agents. But two, they're also kind of an area where we're starting to get a little bit of a preview of the future. You know, Lindy's Swarms came out recently. And Swarms are basically agents that beget other agents, at least in Lindy's case, where they start to do parallel processing, right? So instead of it being an agent that's doing, you know, a data enrichment around a particular lead at a time, it's, you know, an agent that's fragmented itself into a hundred different agents that's doing data enrichment across a whole set of lookalikes all at the same time. And it's all just efficiency.

It's how much more can it get done in a given period of time? And again, you know, organizations are just starting to put these systems online. I think that there's still a lot of work, a lot of customization that's necessary. But I do think that to the extent that, again, companies are looking for a place where they can dive in right now, get their hands dirty, and probably get some pretty clear ROI from agents. Sales and SDR type agents are a pretty good place to look. Yeah, I agree. and also probably out like personalized outreach marketing. Same methodology.

Yep. Yep. I think it's sort of the same bucket. I will say that I am less convinced around marketing content in general. I still think that there is a fairly meaningful gap in quality between sort of like copy that's kind of come from agents right now and copy that comes from people. Not because agents are bad at writing or anything, but this is just, it's an area that involves so much taste.

so much agency and so much, you know, knowledge and experience that you don't even realize that you have, but you happen to notice that, you know, people responded to this one word in a tweet one time in a way that made you never want to use that word again, you know, whatever. It's, it's the closest where there's actually still a big meaningful gap. I think that that'll change over time. I again think that swarms are going to be part of the answer where we run lots and lots of scenario planning. Basically, maybe testing. Yeah, exactly. War game type campaigns with marketing. But let's talk then about, let's zoom back. back out for just a minute as we close out here and talk about given what organizations aren't

doing, what the challenges are, and then what they are doing, what should orgs do? What should they be thinking about right now? What constitutes a reasonable, a realistic, a successful, agentic approach in this particular moment based on where we actually are? Right. So first, let's make sure that they overall improve their agent readiness across the board. We talked about it a little bit when we talked about the seven mistakes, but get your ducks in order, your culture, strategy, have the skills in place, have your tech stack ready, have your agent infrastructure ready. That's like the first and foremost thing that you should do. Anything to add there? I agree. I think that there's a temptation when it comes to agents to think that what we should be doing, the entirety of what we should be doing is picking an agent and deploying it. And I think that that's a big part of it. But there is so much.

infrastructure, new capabilities, new thinking that needs to be done around it. And a lot of that work actually can be done more successfully even than some of these deployments in the here and now. Yeah. And then the second part, and the most important, at least in the context of this conversation, is to really have a prioritized list of eugenic use cases. And you should probably have a list that is much more comprehensive than what you can probably physically do or from a feasibility perspective do right now. But if you start having this list and then you go at it one by one, you will probably be much better off than just selecting one kind of opportunistic one and going from there. The main thing that I will encourage you is to have this list very much prioritized,

not just by value, because in many cases, like we talked before, the highest value agents are the ones that you will probably not want to tackle at the beginning, but some kind of a weighted prioritization between the value, the feasibility, and the, basically, the cost. And then once you have this prioritized list, you can go and start executing them. And we can kind of help you figure out some of the indications for what might be a good use case. So first of all, in terms of a good use case, a good place to start is to see what others in similar industry are doing.

I want to provide one caveat here, like we talked before, that some. Sometimes that can create a bias of you over doing again and again what others have already done. And in many cases, that's not the only thing that you can do. The other thing that you can try and look for are use cases in your business that might be a good candidate for agents. And I have like a few pointers to provide you in order to identify a good use case for agents. Some of them are more trivial. Like first of all, it should be something that is being already done on a computer, a digital. use cases is good for agent. Second, think about use cases where you need a lot of specialization,

but the humans are a bottleneck. So your agent swarms are a good example. Ideally, you would have so many salespersons that you will outreach to anyone and everyone and follow every lead, but you just don't have enough salespeople. So that's an example of a specialization. Another example, very classical one is legal stuff, an agent that goes over contracts. Often you just don't have access to enough lawyers or it's very expensive. So that will be a good example of a use case that require personalization. Other places that are very good candidates are cases where you need a lot of availability 24-7. So of course, customer support, but that also covers all the employee support. Or think of any cases where perhaps you want to do better by your customers, but you currently

can't, either because you're a small company or just don't have these services and consider agentifying them. Another thing that I can offer is cases where you need to have a lot of personalization and you don't have the demand power to personalize. So marketing, we can debate, but there are other cases where you want to handle each and every individual separately. And then a few additional things that I can think about is what about cases where the more data you have, the better the agent will behave. These are often places where when I'm sending you to create your infrastructure. These are the places where you should consider.

So these are also relevant places. Another thing that I'm often asking our customers, and you always say, don't just ask about that, but that's important, is cases where people are disliking what they do for work, because it's repetitive, tedious, and they would have liked to do other stuff. And last two that I always kind of look for in a good agent use case is where the process is well defined. The business process is very clear.

there is a very clear set of policies by which decisions should be made. And lastly, and connected to that is when we can measure the output of agents. And that's why perhaps coding is such a good use case for that, because we can measure whether the code is functional or not. But think of other places. If you're able to differentiate between a good and bad outcome, that might be a very good use case for agents. What type of next step should people be thinking in terms of?

Should they be, you know, getting together, committees to start making decisions differently, should they just be throwing themselves into a first test case? How much should they be focused on the infrastructure buildout versus just actually getting their feet wet with agents right now based on what's available? So it's going to be and it depends kind of an answer. If they had all the resources in the world, so all of them, create a list of use cases, invest in infrastructure and simultaneously start working on pilots of agents because that's the best way to learn and augment both the infrastructure and the use cases. If they don't have all the resources in the world, I would probably be very opportunistic

and say deploy your first agent and learn from that. That's my take. Do you believe something else? I know. I'm with you. I think that there is no substitute for the hands-on learning that comes by actually getting in there and understanding capabilities. I also think that it naturally is going to be get, you're going to figure out all the things that you don't have in place as you go try to actually deploy an agent, right? If your data is not ready, you're going to have to deal with that to get that agent ready. If you start to run into issues of decision making, maybe that's sort of what prompts you to think about, kind of garbrials and governance more broadly. So I kind of think that by going after actually doing the thing, right, starting by starting, you're likely to have all the

other pieces come along with it. Yeah, I agree. And end up overhauling your entire tech stack. Yeah, exactly. Yeah. And get ready for a lot of change in a very short period of time. Newfound, always awesome to have you on the show. Thank you for this. We'll come back and do another, you know, what people are doing with agents in six months. I expect it will be very, very different than what we're talking about today.