TDX 2025: Main Keynote | Salesforce

Foreign.

Who are the Agent Blazers? Well, now, that's a story. Trailblazers. They've led the way.

People who saw a mountain and said, hey, I'll climb it. People who saw a challenge and said.

Hey, let's solve it.

They were pioneers, innovators, visionaries. But today, Today, something new is on the horizon. It's not just about blazing a trail anymore.

It's about building a world where humans.

And digital agents work together. Where empathy meets efficiency. Where intelligence is autonomous. We call them Agent Blazers.

A new breed of problem solvers who.

Cut through the shiny hype of AI and answer the real question of what AI can actually do. I mean, what if workforces had no lim?

What if customer service could feel like.

A conversation with an old friend? What if businesses could make smarter decisions in real time? Agent Blazers see the future not as something that happens to them, but rather something they can create.

Now, look, we've been dreaming about agents for years.

We've seen them in the movies. Robots, avatars, futuristic assistants.

But this, this is different.

This is real. Agents who learn your business. Agents who your customers. Agents who can take action right when.

You need it most.

Agentforce is about empowering people, freeing humans to focus on what really matters. Yeah, our stuff. The best part, the admins, the developers, the dreamers in the Salesforce ecosystem, you're the ones turning this vision into reality. But Salesforce didn't just build this platform to automate tasks.

No, it was built to empower you. People like you to become Agent Blazers.

So who are the Agent Blazers?

You are the Agent Blazers.

And the really cool part, the future, it's already here, right now.

Please welcome evp product and industries marketing, salesforce patrick stokes.

All right, good morning, TDX 2025. Thank you all for being with us here today. Man, you all look so good. This is awesome. Thank you to the thousands of you that are here with us in the room today and on the event. Really appreciate you being here. Thank you to the thousands of us. Where's the camera? There's a camera watching tens of thousands watching online. You know, look, we have quite a bit to do today. It's going to be a really, really busy day, and I'm going to tell you why. Six months ago, we launched Agent Force, just kind of right across the street in Moscone. South. North. Where's Jacob North? I think we launched Agent Force right across the street. And we showed the world what enterprise, what trusted enterprise AI agents could look like in your workforce. And then, believe it or not, just a few months later, across another street, the St. Regis Hotel. In December, we launched Agent Force 2.0, showing what it looks like, kind of unwrapping the Atlas reasoning engine and showing you exactly how Agent Force can reason and think and actually take action. And since then, not a day has gone by, I swear, not a day where I don't wake up and there's some message, some new news report of a new AI model or some sort of new technique or new application in how we use them. And it's just, it's really hard to keep up with. The pace of innovation has been incredible. So that's what makes this year's tdx, I think, more important than any we've had in a really, really long time. Because we all are about to go on an incredible journey together, an incredible journey where we use all of this new innovation. And our job here is to show you how to use it, to show you what it can do and to bring you along on that journey. And my job as the host of TDX of this developer conference is to get you to that innovation as fast as possible. So let's go ahead and do that now. Of course, we have to start by thanking you. Thank you to our customers that have been with us on this journey. Thank you to our customers that are here with us today, those that have already started their Agent Force journey. Thank you to all of the builders, our trailblazers, our agent blazers, developers, admins, our community, our architects. Thank you all. Thank you to our partners, our ISVs, thank you to all of our employees that are waking up every day dealing with this pace of innovation and building so that all of us can use it. We really appreciate it. And of course, when we start any journey, we have to start by just reminding ourselves of our values. This is something we always do at Salesforce, right? Our values of trust, customer success, innovation, equality and sustainability. These are what guide us through everything. And we're going to need it more than ever. As we go into this path, this path where we're bringing AI into our workforce, we're going to need these values more than ever. Now, when we started this company 25 years ago, I wasn't here, but Mark and Parker were here. They started with a really simple idea. They called it the one one one model. This idea that we would give back 1% of the company's equity, their time, their product back. And I'm really proud of the impact that this has had globally, but the number on this slide. I've been at Salesforce for 13 years. That always makes me so proud is the 19,000 companies, the 19,000 organizations that have adopted this model as well and come along with us. And it's because of all of you that we're able to do well while doing so good. And it's been an incredible journey for Salesforce. It's because of all of you, because of our customers, but really because of all the builders in this room, the ones that build and implement Salesforce and bring it to life and build these customer experiences that will do 40 billion in revenue this year. It's just incredible. Okay, but we have to talk about something new here for a second. And I thought about this for a long time and look, this is a developer conference. So we need to get into the nitty gritty. We need to get into the stuff that developers really think about, that they really care, that they really think about every single day and wake up thinking about. And I thought about, what could that be? And I realized it's pretty simple. It's global declining population growth. That was a joke. You can laugh. So look, this is actually not a joke. This is really important. All around the world, countries, their population growth is slowing. Now, as developers, as builders, why should we care about that? Well, if you give me a minute, if you just bear with me for a minute, I'm going to try to tell you why you should care about that. It's actually pretty simple. Population, people, Us humans, we make companies grow. It's as simple as that. And if there are less people, that's going to create a plateau where businesses are going to struggle to grow. In fact, I like to think about three things that are directly correlated. If we think back hundreds of years, there are kind of three things that are always related to businesses being able to grow. One is people we just talked about. The other, this is a little bit hard to think about, is real estate. And the third is infrastructure. Now, people we can kind of understand. Let's think about real estate in order to grow. Well, first of all, if we need to hire more people, we need more office space. But we might also need more stores, right? We need to enter new markets, so we need more stores and infrastructure. Well, we need compute, we need storage, we need to build more software. So those three numbers are always going to go up. And if we think about those three. Let's think about. Let's take infrastructure for a moment, right? Our friends at AWS and Google, they've built this incredible capacity to scale infrastructure. We can now scale the software and the infrastructure that we need infinitely and horizontally We've kind of done the same thing with real estate. It's a little bit hard to think about, but you all have really been responsible for this. Let's think about it. If we want to enter a new market now, 25, 30 years ago, we needed a new office, we needed a new store. Well, today we just need a new website, we need a new application. So we've kind of figured out how to scale that as well. But the one thing that we haven't truly figured out how to scale yet is the people. And that's where you all come in. What if we could use software? What if we could use technology to scale human capacity? That is the promise of agentic AI, and it is creating a digital labor revolution. This idea that we have human workforces really exclusively today. They have fixed capacity. You know, the thing about humans, if you're like me, is like, you get tired, you get burnt out. There's only so many things you could do. But what if we could pair humans with smart, intelligent AI agents that can actually go out and do work on our behalf, that could work with us, that could partner with us. Well, then we would have unlimited experts, not just the agents. But think about what an AI agent can do for you. It can make you an expert. Our customers, they don't have to wait on hold anymore for another human. They can just get the answers that they need. We could unlock growth. We would have hit all three. We can scale our infrastructure, we can scale our real estate through our web applications and through our digital experiences. But now we can also scale our people. Now, to do this, we need something. We need some technology. And it's really, really easy to think. Well, the solution must be a large language model. That's what they're going to tell us here, right? It's like we're going to just use a large language model and it's going to work and it's going to change everything. And look, this is a revolution. This is a revolutionary piece of technology. It's this thing, that little piece of software. It's not that little, it's actually pretty big. But it's a piece of software that can understand human language. It can converse with you, it can almost even fool you. So we'll just slap a large language model into it. Well, it's not that simple. You see, a large language model, it's not enough. It's certainly not enough for digital labor. We need something much bigger than that. If you think about a large language model for a moment, it's a lot Like a cpu, right? A CPU was equally revolutionary, right? It could read and understand computer language, binary zeros and ones. It could run really complex computers computations. It absolutely changed the world. And today I could put a CPU in the palm of your hand. In fact, I tried to get one and I thought it would be cool. I could pull one out and have a CPU in my hand, but I'd mess up all the pins. But you could put a CPU in the palm of your hands and you know what it would do for you, you know what it would do for your company? Nothing. It wouldn't do anything because the CPU needs to be part of a larger system. It needs to have a motherboard and storage and memory and an operating system and applications built on top of it and peripherals so human beings can interact with it like a keyboard and a mouse and a computer screen. It needs to be part of a system. A large language model is exactly the same. So what kind of system do we need for a large language model? Well, there's three things. We like to call this our trinity. There's three things that you're going to need. The first is data. You've got to get data to these large language models. You've got to get your business data, right? We've been talking about this for a little while, but it's really important. These large language models, they've been trained on the Internet. They don't know anything about your business. So we need a way to connect the data. And not just your structured data, but all of your unstructured data, your knowledge articles, your metadata, your semantic data that describes what that data is in the first place so that the AI can understand it. So that's the first part of the trinity is your data. The second is you need your apps, so you need all of your line of business applications, your industry applications, your analytics. You need all of your APIs. You need to be able to connect all of that so that your AI can actually take action. And then the next thing you need, of course, is your AI itself, your agents. We need a way for the AI agents to connect those APIs, but we need a way for it to also connect to all of that data. We need some technical techniques like retrieval, augmented generation. We need things like vector databases. We need the ability to loop and the ability for the AI to actually reason if we want it to come up with good and accurate answers. And these things are not terribly easy to build. They're not terribly easy to bring together yourself. And that's why we've done it for you. We've brought all of this together on one trusted and deeply unified platform. And we've added all of the trust layer, all of the security and permissions and governance that you need. Because if someone in your organization asks, an AI asks an agent a question, you want to make sure that the answer is tailored to that individual. You don't want to give away secrets from all of your data. So we really need to think about how do we apply all of that trust. But if we have one unified platform, as developers, as builders, you know, look, we're always a little bit worried about vendor lock in and do I have to do everything with one company and. No. And Salesforce knows that. And that's why we've built this entire platform, this deeply unified platform to be as open as possible possible. You can bring any LLM, an LLM that works for you may not work for your organization. You can bring any LLM, you can bring any system, you can bring any API and hook it up to this new platform. You can bring all of your data and you don't have to physically move the data. You can use 0 copy and connect the data leveraging the investments that you've already made in data lakes from our friends over at Snowflake and Databricks and others. And of course you can run this whole platform in any geo that you need. Now we put this all together, we put this all together into one platform, one trinity of data and apps and AI agents. And we call this platform AgentForce. This is our digital labor platform. It's trusted, it's easy to deploy, it keeps humans in the loop, it eliminates that hold time. This is one deeply unified platform, making it easier and faster than ever to build these agents. Now customers that are using this platform that are transforming all of their work, their customer workflows, their employee workflows, and transforming everything with agentic capabilities and reasoning. We call these companies agent force companies. RBC is a great example. This is one of my favorite. They have these wealth managers. These folks wake up every morning thinking about how do I advance my customers portfolios, how do I help them manage risk and help them find new opportunities. That's a hard job, especially when you have thousands of customers in your book. Well now AgentForce is a partner with those wealth managers. It's helping do all of that work for them so that they can call and talk to their customer about exactly what they need to talk about that day. Remarkable is an agent force company. Remarkable is awesome. I use this every single day. It's these new E Ink paper tablets. They're awesome because it strips away all the stuff that we usually have on our tablets and it just focuses you on writing and reading. Those are good habits, by the way, writing and reading. I encourage that instead of scrolling and remarkable does that as well. It's an incredible company. These are amazing devices, but their problem is they're growing so fast and they wanted to keep their customer service just as good without having to grow all of the humans that they have, they wanted to bring Agent Force in to be a partner so they could provide that white glove customer service experience that they need while they were growing. Now, how does this all work? Well, it sounds pretty sophisticated and frankly, it is pretty sophisticated, but it's actually pretty easy to think about how this all works. It starts with an input. Now, we've been used to seeing those inputs as chat inputs, right? You come and you talk and you're going to see that today, but you're going to see much more than that. You're going to see inputs from APIs, from events, from things like change, data capture. We're going to be able to connect this thing from anywhere. Then reasoning and there's a little fancy graphic there and it's like, trust us, it reasons, there's a little black box. I'm gonna break that down for you in a moment. Then there's action, right? We want it to be able to actually go out and do things so we have our action. We can even enrich that action with tableau. In other words, we can do the action and then look at the outcome and tell you the outcome that that's had on your business. And then of course, there's the outcome itself, which in some cases might be again, another turn in a chat experience, but it might also be some sort of execution, execution in another system. Now, that reasoning there, how on earth are we thinking? Well, it's complicated, but it's not. It's really just a loop. It's just a loop. It just loops. It thinks the same way a human does. When I ask you a question or when I give you a task, you don't just immediately come up with an answer. You think, do I understand the question in the first place? What data do I need to understand this question? Maybe now that I've got some data, I realize I need some other data. You're just looping. ATLAS is doing the exact same thing. And it's because of that that we're able to deliver incredibly trusted and accurate answers. Basically, the more you Think the better the answer is. Funny how that works, right? Okay, now, how are customers getting started? Well, we've done thousands of implementations, not just with our customers, but with ourselves. You'll see this later today with help.salesforce.com and the simple way to think about this is the first agent you want to try to build. It's really simple, just an agent that can answer questions, but not just any question, right? ChatGPT can answer any question. What you want is an agent that's actually hooked up to your data, that can answer questions about your business. And guess what? That's not magic, it's not free. What you're going to realize is some of your data, it's a little bit out of date. You're going to have to go back and fix some of that data. You're going to have to update it, clean it up a little bit. But once you're done, you're going to get this incredibly intelligent agent that can actually answer questions about your business, not just for your customers, but also for your employees. The next step from there is you're going to realize really quickly it'd be cool if this agent could do something, not just answer questions, but do things. So you're going to want to hook up your APIs. And then the third thing, which we haven't really talked about, but you're going to see it today, is the ability to take action proactively. That's that third stage of your agentic maturity, being able to build an agent that you put inside of your organization that can just take action and go out and start working on your behalf. Now how do you get there? Well, we need to move from traditional companies, companies where all of our workflows, our employee and our customer workflows are hard coded, right? With rigid logic. We need to move to systems that reason and learn really agentic logic, right? Non deterministic. We need to move from disconnected data systems to truly unified data systems. So how on earth are we going to do all of that? It seems like we have a lot to learn. Well, that's why we're here today. The good news is, and we're counting on you here, the answer and how we're going to do all that, of course, is you, our agent blazers. You are the ones that are going to join us on this journey. You are the ones that are going to help move us from these rigid old world traditional software companies to these new agentic software companies where everything is run authentically, where we have humans and customers working together and to do that, we're excited to launch our all new agent blazer community. We are seeking 1 million agent blazers globally by the end of the world. End of the world. Jeez. Amazing. See these jokes, These jokes write themselves by the end of the year or end of the world, whichever comes first. It was good. I was feeling we needed a moment of levity there. And we're going to talk a lot more about all of this new content and community today. It's going to be crazy. Okay, now we're about to get into all of the technology and the innovation, but I know what you're already thinking. I know what some of you are thinking because a lot of you have thought this out loud and gone to Twitter and said, hey, Salesforce, this is all awesome. This is great. You keep showing us all of this new technology, but when are you going to let us take it home with us? When are you going to give us this technology so we can play and experiment? And so before we show you all of the technology today, I wanted to just close that gap and announce that Agent Force and data cloud are now available in our developer edition. So you can get it, you can sign up, hit the QR code, you can even go downstairs. You remember when we built agents at Dreamforce and you would build them and then your scratch org would expire like nine hours later. Well, now you can take it home with you. Much better, right? You get 150llm outputs per hour, one data space and 10 gig of data. And then here's the important part. This thing will never expire as long as you use it every 45 days. So just log in every 45 days and you'll be good. And if that's too hard, just write an agent to log in for you every 45 days and that'll work as well. I'm not even kidding. That can work. Okay, so we have three things we're going to walk you through today. We are going to show you how you can agentify. Not just through chat, but we're going to show you how you can agentify every workflow. Customer workflows, employee workflows, you name it. We're going to show you importantly how you build these things with confidence. What does an agentic development life cycle look like? And I'm really excited we announced this yesterday. You might have seen it. We're going to show you our all new agent ecosystem. Okay? So a lot to do. Thank you for bearing with me. Thank you for laughing at my jokes and slip ups. And I'm excited to announce our EVP and GM of Agent Force, Adam Evans. Adam, over to you, buddy.

Cool. Thank you, Patrick. All right, tdx.

All right.

This is my first TDX in the world and I thought it was appropriate to open with a small confession. You can ask anyone on my team. I am a developer at heart. I'm a builder just like you. And so I'm really excited. Not about that alone, but also that we have a tremendous amount of new functionality for everyone here to build new agents and do much more. But first I want to talk a little bit about software. Everything about software is changing. How we build it, how we experience it. And you can think about this idea that we've seen for decades where we have data and logic and ui. You could call it like a model view controller or three tier architecture. We've had different names for this over time. This is fundamentally changing. Think about data with structured data, relational databases, CRUD applications. This is where we've been. Now we have new tools finally to be able to unlock the tremendous value in unstructured data and not just in terms of vector databases and being able to query and understand and reason across it, but applying that agentic reasoning technology to also onboard product with data to understand meaning. It's unbelievable. And that also moves into logic. Traditionally we've been writing code, it's rigid, it's all the conditionals, if this, then that as you all know. And now we're moving into a new world where we have not just deterministic but more dynamic reasoning across it that can be instructed with natural language, not with code, and can handle many more permutations at different conditions out of the gate. And that leads me next to ui. And this is probably one of the most fundamental changes that's happening where we're thinking about instead of building kind of expensive and specific UIs that are kind of these boxes that we're moving in with clicks. We're moving into a world where UI can be developed at incredibly low cost as the cost goes to zero. The agency reasoning can build UIs as you need in the context that you need them, with visualizations and more. And it's moving from things like boxes and clicks to conversation and to voice and to be more multimodal. Everything about software is changing with agencic reasoning. And as Patrick mentioned, in December we launched Agent Force 2.0. That was massive leaps forward in terms of the ATLAS reasoning engine to be able to reason across things, but also lots of work that we did with data with better understanding, with things like Our enriched indexes that let the reasoning of the agents understand much, much more. For larger sums of data. It was a true unlock and I think about a lot of data. I think about 700,000 articles. You heard the story earlier from help.salesforce.com we have 700,000 articles about every product, every detail from years of information in Salesforce that's now plugged in to AgentForce, answering questions about 40,000 questions every week on help.salesforce.com and by the way, it's answering at an 83% resolution rate. And of the time that the agent force actually engages, only 3% of the time does it actually escalate to a live human. It's an incredible operational story and we see this with all of our Agent Force customers. There's only one thing that I think about here which is can we do more? How do we think about bringing this awesome technology and moving beyond the chat window? How do we move outside of the box? How do we think about allowing agents with the reasoning capacity to create new UI and think about applying it to logic non conversational experiences? And how do we give you, the developers, more control over what your agents can do for every workflow in a way that your enterprises demand? And we've been really busy at work. We've got lots of new features and we're today very excited to announce the next installment which is agentforce2dx. This is specifically for you, the developer edition. And this has got tons of features here. The ability to have more control over your agent so that enterprise can count on it with features like variables that allow deterministic guaranteed logic to be brought into your agent without sacrificing the dynamic nature of what agents can do. It allows you to build your agent force to move beyond conversation, to be proactive as opposed to reactive with things like triggers and bringing it in for variables and do background work and have deeper reasoning over longer periods of asynchronous tasks. And also it allows you with features like surfaces, to bring your agents more connected to the interfaces in which we're using them as users. More awareness of what's happening in your mobile application on your website, to be able to take action on those, not just talk and be able to create more custom UI and components to bring the experience to the next level. This is a developer conference, so instead of just telling you about it, I want to show you one of the best ways we can do do that is through our customers. And I'd like to introduce by the way, Vivint. Thank you An Agent Force company right here that's paving the way and we're going to tell you the story of Vivint and then we're going to do some live upgrades getting into DX and what we can do. First let's watch a quick video.

There's no place like home. It's your family. It's what you protect. It's all about peace of mind. That feeling of I know I'm secure. I know the people I care about the most are taken care of. It is everything, everything that matters. Vivint is a leading smart home provider. On average we have 14 or 15 individual devices in our customers home. Our platform brings all those devices and sensors together in an intelligent way and provides a magical experience for our customers. Vivint's growing rapidly and as we grow we we need to make sure every customer gets that tailored experience that uniquely meets their needs. With AgentForce we're giving our customer service team a digital labor force helping every customer know we are there for them. 24. 7 the AI Revolution this era that we live in is really the most exciting time I've had in my career. It became very clear to me that Genai could transform our customer experience in a way that we have not seen. We've got to find a way to leverage this. Our bias was towards hey let's go and build this. We had to orchestrate all of that data and segment it. We went and built our own rag. We had to build a platform before we even started getting any value. Agent Ford did that heavy lifting right out of the gate. We don't have to move data around. It's native, it's in our platform. AgentForce allows you to piece together very quickly a set of technology that has taken people well over a year to build out equivalent platforms. Agent Force is quicker, faster, better. We got business value immediately. AgentForce spoke for itself. AgentForce has several instructions that we've told it about and it's going to take some action based on that. And so what it's doing now is it's actually looking at our knowledge articles and it's also pulling in specific information about your system and combining that with our CRM data and it's coming up.

With okay, what's the next step that.

We should take to troubleshoot it.

So it looks like it's asking me what color the status light is and that just lets us know what's going on with the system. And I can see that it's white.

If you can see here it's actually going through this reasoning part where it's saying it looks like the white light is indicating that the camera has power, but it's probably disconnected from the Internet. Could you go to your WI FI.

Connection and try power cycling the router?

What's great is that AgentForce isn't data dumping and overwhelming the customer. It takes care of the customer in a simple, friendly way.

That Jackbot just can't do this type of stuff. We have a tremendous responsibility to our customers. AgentForce allows us to provide a very personal customer experience that's scalable. This is a technology that is truly revolutionary. It allows us to already do the impossible. When you look at the business and engineering value that we're getting out of just rolling that solution, it's like a cheat code in a video game. Seems too good to be true. The investment in Agent Force is one that pays off now and it's going to pay off in the future. The sky's the limit.

All right, so we're going to dive in to Vivint and we're going to make this interactive. And I'd like to thank and welcome our demo advisors, Stefan and Irene to help us do that. Thank you, guys. And we're going to start off with Vivint's website right here. And this is any customer Vivint can go experience agentforce, their agentforce. Right now, the bottom right hand corner, you can start your chat conversation here and you can ask questions about this. For example, I have an issue with maybe a device disconnecting. Can you help me troubleshoot that? And this is connected and grounded in their data. So their troubleshooting articles, their knowledge about their products, it understands Vivint and it understands a bit about me as a customer, but it doesn't have all the data. And if we contrast this a bit, if you were to email in or call into the contact center of Vivint, what does the human rep, what does the customer service rep event have? Let's flip over to their view of what they see, can understand what's possible. And this is a beautiful view of the service console. You can see in the left hand side, Martina's household, bringing in lots of information about the customer they're talking to. We've got the devices here that are in that household, that they purchased, that they're online and their status. And if we scroll down a bit here, you're also going to see eventing data and all those cameras, the thermostats, all this stuff, these eventing level detailed telemetry data that's coming through Data cloud. So the human service reps have access to lots of data. They've got their kind of heads up display to help the customer. They've got all kinds of buttons and actions they can take to help service that customer. And so we asked ourselves, what if we had that same data that the human reps had, we could connect it to agent orders. What if we took those same actions and the same policies that the humans rep and we connected to AgentForce what was possible. You got to do this in the right way with the right control. So let's go ahead and see what's possible right now. So we're going to move over to agent builder and that smart care agent. This is their agent that's customer facing. We can move into it. And you see the topic at the very bottom, telemetry data. This is now connected to that event level, the device that's emitting what's happening in the health of those sensors. And you can see there's an action there that connects it through data cloud. So we're allowing our agent to actually search the data that's coming in from all those devices. Additionally, it's a lot of data and we might want to analyze it. And AI is pretty good at analyzing data. But having tableau is also very useful in terms of being able to do deeper analysis and visualizations and more. So we can bring in tableau directly integrated here. Now, before we try this out, I want to point one thing out. This isn't just general information, right? This is, you know, my household's telemetry. The device is in my household. This is private information. So we want to make sure we can control what the agent can do. And we all know the best way to do this is to go write an instruction and just tell it with really nice words. Pretty please, let's prompt it. Don't send the wrong data to the wrong user. Of course, that's not what we do. Don't do that. That's not great with prompting. We need a more guaranteed way to do this. And so there's a new feature called variables that allow us to start bringing in deterministic logic directly into the reasoning engine. So you have structured information, for example, the user ID or the profile that's happening in this session. And now you can take the structured information and with a feature called filters, you can create expressions that say, hey, look, if they're logged in like we've done right here, you can create a filter based off those structured variables and take that filter and apply it to any Capacity of the agent. So that telemetry topic that let the agent actually access that customer's data will only be available if they're logged in. That makes sense. There's no way the agent can hallucinate and get access to their data. There's no way that that user could ask really nicely and get the agent to do something that you don't want it to do. So new levels of deterministic control that don't stop the kind of awesome capability of the agent to reason and be dynamic across many contexts, but do control it the way that you want. All right, so let's go ahead and ask a question. Now as this is thinking, you'll see the selected account. Notice we don't put an account ID in. So how does it know that those variables, you can also inject them so you can save a profile, saving you a lot of time by the way, as you're doing this. Start from state. It's a great testing harness. You can see we got the response here. Now, as we asked about the Martinez household, what you can see is it doesn't come back with just kind of one event. It's actually looked at multiple bits of telemetry here and you can see that it's experienced multiple disconnects recently. And the agents actually this nice visualization from Tableau decided when it received that data that's the best way that I.

Should be able to see it.

So it's creating a visualization right on the fly for me. How does it do this? Let's jump over and look at kind of the play by play, the thought by thought reasoning and the actions that happen. And we can start at the top and you see that it's actually injected who we are again and it looks up our account so it understands that we're the Martinez household. And that now allows us to go into data cloud and search across all the device information about what is happening at the Martini Salsa and getting that data back. Now the agent can see those events, you can see that output there, that little bit of JSON could go into it. And the agent understands now that I need to be able to show this trend or this kind of insight to my user. So it's choosing to have Tableau come in here and it's looking at this and saying, can you go ahead and create this visualization? This line chart and what's happening here is quite amazing. You're seeing data with AI and agenic reasoning and UI with the tableau all connected from just one query. That's Happening on the fly with no code. And this is something that only a deeply unified platform like Salesforce can make this easy. And it's fantastic. Now here's a question. Do you think that we can do more? Can we do more with this? What do you think? All right, so one thing that I think about is this is really great, but I had to ask it. I had to say, hey, what's wrong with my house? Is there anything going on that you should know about? And it can connect to my data. What would have been really great is if my house could have actually talked to the agent and then brought me into the loop as the consumer. Only if there's an issue.

Right.

And to do that, we need to have agents be able to work in the background. We need to be able to have them be triggered and work. And the best way to do that is in flow. Do we have any flow users here? All right, so for all those that just said yes, you know, this is what flow looks like. It's always nice and clean and simple. I'm actually kidding. Stefan, can you zoom out? Actually, let's see this.

Yeah, that's all right.

This is what flow typically looks like, right? And so this is. Let's just talk about this for a second. This is logic. This is conditions. This is code. And by the way, what are these things doing? These are saying, if it's this device or this event, go do these things. What happens? You know, Vivint's launching new products all the time, new devices, new things. This logic's gonna grow. There's maintenance here, right? But instead of putting all this logic and reasoning in this way, we can start using agents now to do this. So instead of having conversation in and conversation out for an agent that's talking to us, we can have data in and inputs in and action out, because we can put agents directly into flows now. And so this is now the same flow, but with Agenic reasoning as a singular step. And if we click on that, you can see right there the inputs. It looks kind of like a prompt. Any of your flow variables, you've got, who's the customer, what's the event, what's happening in the flow. You can actually push this into the agent and allow the reasoning to happen inside of it. Which means you don't have to code and describe every single possible path. You can say, hey, here's an event. Let's go look at the telemetry data and see if there's a pattern you can say based off of a situation. Here are My policies to handle things. Here are your potential actions. Notify the user or don't notify the user. All of those things are possible at the agencic reasoning level. So why don't we go ahead and let's go simulate. Let's run this flow. We'll simulate an event going through. And we've set this up so it's probably at the tipping point for the Martinez household. And you'll notice that when we run it, it isn't something that sends a notification right away. And that's because the agent is looking up the information. It's going back, it's doing research in the telemetry, it's making a decision and then it decides. There we go. For push notification. That's now bringing agentic reasoning and experience directly into Vivint's beautiful native mobile application as a new surface. And we can open up this message and the first thing that you see is this is not a generic notification, something disconnected. This is intelligence, this is action. We're seeing a digest here. Hey. Hi, Emma. And then you could read down here. Our device shows that WI fi has been going in and out over the last week. It's actually looking at telemetry because it was instructed to do that. That's what the agent was told to do. Look at the bigger picture. Is there something more systematic going on? Look at who the customer is, are they a great customer, what devices they have. Here are your possible courses of action. And you can see that it's not just summarizing kind of what's happening to me as a customer, but it's also giving me a suggestion of what to do next, which is scheduling appointment. It's looked up the free busy or the available time slots. And it's using our new UI componentry here with our time picker to create an effect that I can just tap a button to schedule a field service appointment as a consumer and this is an awesome experience. This is a contextual message with action with one click, with ui, all that coming together and it is fantastic. This is a taste of what's possible when you bring agency into the consumer experience. But consumers are not the only people having fun here. We also can apply all of AgentForce and all of agency reasoning to help employees in every workflow. And if we want to kind of break that down, I just scheduled an appointment for a field tech to come to my house or the Martinez house, fix it. And if we say how could we help that field service, that technician with agents? And the answer is when you Wake up. What are you doing? You're thinking about what is my agenda? What are all my appointments a day? Do I have all the parts that I need on my truck? Because I don't want to show up to appointment without the right things and have to reschedule. Nobody likes that. And also, once I get busy with my day, when I'm going from customer to customer, what am I busy doing? I'm fixing their problems. I'm building relationships. There's a lot happening in the middle of the day once the day starts. So how can we make this more useful and helpful? Having an agent, an agent force along the way. Let's start with that first problem. Waking up, making sure the right parts are on the truck for the day. Okay, so we can go into. Now we're into another agent. This is our field service agent. And understand parts and inventory and things like that. We now need to connect to our erp. So we need to connect to something outside of that. And the best way to do this is with the mulesoft API catalog. So right here I can have my agent connected to be able to order parts, understand inventory and more. And maybe we want the technician to also be able to do analytic queries and visualizations with that tableau. So let's go ahead and add in some tableau actions and then instead of notifying them in that push notification, maybe we want to notify them proactively in Slack instead, where our field texts work.

Right.

So let's go ahead and be able to send a message here and we'll save this and let's go. Let's actually kind of day of actually, if I could ask, you want to do a little role play? I'll be the technician. And Stefan, do you want to be my next appointment that I'm coming to?

Absolutely. So I'm a very happy Vivint customer. I know. I've got a service appointment today and I've got a notification right on my Vivint app.

Hello. We're due to arrive at your home within the next one hour. Is there a gate code that our technician needs to get to the home?

Yeah, it's 5321.

Got it. Would you like me to send you a link to track your technician?

Yes, please.

No problem. I will send you a link to your technician's estimated time of arrival.

All right.

So we're seeing here.

Brand.

New interaction that's deep within the mobile application. So this is part of what's part of Surfaces. And also you're seeing voice, which is really incredible. And the ability to have custom UIs with maps, that's fantastic. But you know what's happening right now? That's the consumer experience. For my next appointment, what am I doing as the tech? I'm helping a different customer.

Right.

I'm actually busy with another customer. And what is happening, I have a teammate. AgentForce is literally one step ahead of me with my next appointment. Making sure that they're home, that they're there, that I have access to it. It's working with me as a team throughout my day. All right. And now when I'm leaving my appointment, I'm ready to go to that Martinez household. What am I going to do? I'm going to open Slack. All right. With our work item channels, I can see my next appointment as a channel. All the context is there and my field service agent has beat me to it. It's already got my pre brief for the appointment. So I can see that I'm due to meet Emma Martinez. A little bit about the customer that the issue is her smart hub keeps displaying the red light, looks like it needs to has an issue. And then a little bit about the customer that she's been a customer for five years. It's giving me kind of a digest of what I need to know to go help that customer. And then ultimately it's also Olek is choosing to use also a tableau to show me the telemetry data. So it's able to say, in this case, I've gotten some information. This might be useful to send you the trends of what I've seen for disconnects. And then additionally there's some actions and you can read here that it's based off of what it's seeing. The issue is with these disconnects, it's made sure that I have a smart hub on my truck because I might need to replace it. It might be a networking issue, might be a smart hub. So it's like you have the parts on your truck that we likely think are going to be needed for the service event. And also of course, the gate code, as you just saw. Now this is interactive. I could come back and I could say, hey, what's a smart hub? I'm new here, I'm a new technician. How do I install it? Have access to all that information as a new employee to help me onboard. Or maybe I'm going to say, well, this is an interesting trend, but it's only for a few days or something. Can you actually give me three months? Or what's been Happening or what other devices. Go back to the data and much more or take actions. For me, I'm running late. Can you let the customer know? So it is literally a partner agent forces your partners. You're doing work. And this isn't just a team where there's one agent and working with a human. In Slack, it's a multiplayer game. So when we check in we also can have things like a sales agent coming in with access to the same data, the same telemetry, the same customer information, the same understanding of what parts are on my truck, what campaigns are we running now. And it can notice, for example, that the Martinez household has a first generation camera. And I happen to have the latest fifth generation smart camera here that I have on my truck. I'm not going to need it for appointment today and it's authorizing the ability for me to do a free installation and give them a promotion while I'm out there. This is the power of agent force coming in with more control, better experiences, background, working across all the services. It's really any team and any workflow and we can't wait to see what you build with it. That back to you, Patrick.

All right. You know, I gotta say, nobody else is doing that. Nobody else is showing demos like this. I'm just, I'm not seeing it. And I think it is this deeply unified platform, our data, our apps and our agents, all deeply unified that are allowing us to do this. I mean, look at all of this incredible innovation that we just saw. This is why it's not just Vivint, but 4,000 other customers in just a few months that have signed on and are now building these agentix solutions. It's just really awesome. But there is a problem and that problem is actually Adam. You see, the problem with Adam is he's very confident and everything that you just saw, Adam built right in production and that's kind of stupid. So we need someone to help sort Adam out. And for that I'm excited to welcome up Alice Steinglass who's going to walk us through how to do this professionally. Alice, over to you.

Thank you, Patrick. I'm with you, Patrick. It's incredible, but we all know it isn't perfect. I was hanging out with my 17 year old this weekend and we were playing with the big online models. We're using all these cool new tools. We downloaded Llama and Deep Seq Lite. We fiddled with it in Python. It turns out AI can make a pretty good new wave song. I thought it sort of sounded like The Cure. My kid had never heard of the Cure or New Wave. So I said, hey, I'm going to do something for you. And I asked a couple of the models if they could make a Gen Z joke that would resonate in his Discord server. Nope. I'll roughly quote Mom. I wouldn't post a single one of these. And that's a challenge. That's a challenge for everyone in this room. Because even if you don't need a Gen Z joke in your office.

There.

Are some things where agents are going to accelerate us and it's going to be amazing. And there are tasks where honestly, the agents should work with humans who actually can tell jokes. And to figure out which is which, you're going to need tools. So today we are announcing the lifecycle for agent force 2dx. It's a mouthful. Basically, we're taking the entire software development life cycle and we're going to add capabilities for you to build, test and deploy agents with the Agent Force life cycle because it's different. How do I build with natural language? How do I test things that are non deterministic? How do I deploy these, Monitor them, debug them? We're going to need tools across the lifecycle. You're going to want to see these hands on. So we've got Stefan once more here at the demo desk and everything you're going to see right now is live code. Some of it's in dev preview, some of it's in pilot. We want to test this before we release it, but everything you see here is going to be released this spring. So let's get started. Should we fire up a scratch Org? That's a great place to experiment. So we're going to start with the beginning of the development cycle, planning and ideation, and we're going to start with a hypothesis. Vivint here has a customer referral program today and they're wondering, could I use an agent to make that customer referral program better? Would it help drive more adoption of the customer referral program? I don't know. Ryan doesn't know. So we're going to go test it out here. When I start building with brand new technology with new capabilities, it's hard to start from scratch. So we've got a bunch of tools to help me get started fast here we have a bunch of templates. I can start with an SDR agent, I can create a campaign optimizer agent, but there's no customer referral agent here. So I want to show you something new. I want to show you how we're going to use AI assistance to help you build custom agents fast. When I create an agent with generative AI, it changes how I build. Instead of specifying every detail, I can focus on the what, what are my business requirements? And the AI is going to help me figure out the how. How do I do it here? What it's doing is it's taking those business requirements and it's figuring out what are the topics that a customer referral agent should be able to do. And it's going to pull those in here. And Stefan, can you dig in on one of those topics? Great. Inside each of these topics, we have suggested actions that it's going to take. And this is one of my favorite parts. Do you see that? Calculate referral, balance action. That's a flow. You have a lot of flows in your organization today. Some of you have built dozens of flows, hundreds of flows. I know some of you have built thousands of flows and APIs and apex classes today. And when the AI assistance was helping me get started with this agent, it wasn't starting from scratch. It was taking that business requirement and it was searching through my organization for actions that I had registered as agent actions so that it could find that flow and automatically include it as part of this agent definition. Okay, so let's click next again. And now I can just go ahead and edit or keep the suggested description and instructions for what I want my agent to do. And then I've got to pick the channels. Where's this agent going to work, Stephan? Let's have this one work on Slack Web and email. Okay, now we're going to add data and once again, we're not starting from scratch. We have a lot of data in our organizations. Vivint has pulled in all of this IoT and sensor data. They're streaming in all sorts of data into Data Cloud. Maybe you've connected to your Snowflake instance or databricks and you're pulling it in with zero copy that data's here and we're going to make it easier. So if you have an unstructured document that you want to pull in as part of creating your agent, like here I've got a referral program description, basically just a PDF that describes what this program is. I can just include it right here. I can upload it as part of creating my agent. This actually takes a minute to index. So Stefan and I uploaded this one this morning. He's just going to refer to it right here and then we're going to hit create Agent and that's it. We already have the first step of building done. We already have an agent that we can get hands on with, that we can try, that we can test, that we can play with. So Stephan is going to ask it a few questions. And while he does that, I want you to pay attention to that middle column right there, because this is how we understand what our agent is doing. And that's going to change too. I don't need to look through log files and error codes to understand why the agent or why the computer in the past was doing what it was doing. The agent's going to explain its reasoning in natural language. It's going to tell me why it picked that topic, and then it's going to go make a plan and it's going to have some actions, those same actions that you saw before, that it's going to execute. And when it finishes executing those actions, how is it going to display the result to the customer? Again, I don't have to hard code exactly what the padding is and what it should look like. It's going to be able to use reasoning to say this answer probably best displayed as a movie. So let's just put the movie right there and you can turn that on today with adaptive responses. These adaptive responses, not just pick a movie, but some of the things that Adam was showing you earlier today. It can pick a form or a catalog, cards that it can rotate through and it does it across surfaces. So those will show up in WhatsApp, but they'll also show up on the web. And if you want to be more precise, I know some of you do, in Dev Preview, you can build your own lightning web component that displays the answer exactly how you want it displayed. So you're not just saying what you want your action to do, you're saying how you want it to look. So you can make that pizza tracker that Adam showed you. And all of this, that lightning web component, those flows, those topics, the afghans, the instructions, all of it is metadata in your org, which means we can do all the things we do with metadata. We conversion it, we can play with it, we can edit it. And we're doubling down on our tools for metadata in scratch orgs and sandbox orgs. I'd like to introduce you to the new DX inspector here. I can see all of the metadata from my agent and I can jump in to the builders and the specific metadata for my flow, for my topic, for my prompt right from here. And some of you use source Driven development today and You've got a good handle on exactly what's going on with all of the metadata and all of the changes. And some of you are working in your org and it can be hard to keep track of what are all the changes we made. So here we also have a change tracker. So we can see exactly. I got some applause here. Yeah, I think that one's. I'm excited about that one.

Thank you.

So we can see exactly what Stefan and I have been changing for this agent and we can also see the changes that AI assistance is helping us make. So we saw a little bit about how we can build as low code developers, but I said that this was going to work for low code and pro code. So, Stefan, today we are also announcing the Agent Force module for the Salesforce cli. There's a lot of words. Can you show us how pro code developers can build agents?

Absolutely. And of course we're going to be doing this in VS Code or Code Builder. So we now have an agent as part of our extensions in the Salesforce cli. And it starts out as simply as typing SF Agent create. And then the same way we just created an agent using AI or from a template, we have access to create agents right here from the command line. Now we've already created ours, so we actually want to pull down that metadata. So we are going to go ahead and retrieve that agent down from the org that we just created. But you have got to keep in mind we are not just pulling down the agent, we are pulling down the references for the actions. We are pulling down the topics, the instructions, everything right here into our cli. So what you will see here is we have all of that great information, the plug ins, the functions right here for us. Now there is one more thing I want to show you and it is that we can actually attach and build actions. We have an Apex class that's already in this project, it's not yet in our Scratch Org, and we can go ahead and attach that here and run that deploy right at the same time we're going to go ahead and create that action. It's going to map the inputs and outputs from the Apex class and it's going to save that right here in our org for us.

Great. We built an agent with low code, we built an agent with pro code. Let's move on to the next step of the software development life cycle. We need to test this agent and it's going to be different because we need to test something that's non deterministic. So Stefan can You show us how we would do this with pro code.

Absolutely. So it's going to start out with a test suite that we've pre generated here for the demo today that just takes in some inputs, it takes in some outputs, and we're actually going to attach that to our test runner and generate that test. So I'm going to go ahead and get it started here and we're going to run that test and it's going to be a little different than we're used to seeing.

Yeah, it is. So while it's running, I want to talk about what you're looking at here, because do you see that test case right there? It doesn't look like a standard Apex unit test. We're not taking one input and mapping it to one output. I'm not checking to see if a variable is true or larger than some value. The input is human language and human language is wild. People can say anything. So we have an utterance here that's just a natural language. And the output, again, it's not a variable. I'm specifying an outcome. I'm talking about what do I want the result to be. And we can't just test it with one. We're going to have to test this with thousands of different utterances and we're going to have to do stochastic testing, set probabilistic thresholds for what success looks like. And I want all of these new testing tools to work as part of our CLI, part of the test tools, the DevOps processes that I use today. And so here they're coming back with just passes or fails. If it was fail, we see a fail here, but it looks like Stephan, we passed. Yep, excellent. It's not just pro code developers who need these tools. Low code developers need exactly the same tools. So this is testing center. And here, because our pro code and low code environments are connected, I can see those same tests that Stefan was running in pro code and I can run them here. But low code developers in the room, you want to be able to create your own tests too. So here I can upload a CSV file, maybe I want to scrape a bunch of comments that we've gotten from our bots on the website and use those as a starting point. Or I can write my own tests. Or again, we have AI assistance. So Stefan, let's get the AI to write some tests for us. It's the AI, so we don't have to write 20. We're going to do 2,000 tests. So I'm going to get it to write 2,000 tests for us, and let's generate those test cases so that we can use them here in low code. We've built our agent, we've tested our agent. Now I want to move on to the next step in the Agent Force lifecycle. Let's deploy this agent. And to do that, we're gonna head over to DevOps Center. And here our low code and pro code developers can work together, connected to our processes, our release processes, our source code. We can use it to promote things through the pipeline and make sure we have the right checks and balances in place. So, Stefan, why don't you go ahead and promote this to the UAT environment? And we just got to the point where we could get it into a UAT environment. We could do this deployment step in 13 minutes and 32 seconds. That was fast. That was fast because we had AI assistance, but it was also fast because we weren't starting from scratch. We were using all the things that were already in the environment. We were taking advantage of the data and the flows that we'd already built so that we could get going quickly because we need to bring it out to more users. It's great that Stefan and I can play with this, but, Ryan, I want you to play with it. I want to get it out to our business users, I want more people to get hands on with it. And then we want to deploy it to more people. Maybe everyone in this room should check it out. And then after everyone in this room has given us some feedback and we can see what's going on, we should deploy it out to everybody online watching us. And as we do this, as we roll it out to more and more users, we need to know what's going on, is it working? What's working well, what's not? So let's move on to the next phase of our development process. We're going to go into observe, and here we have new tools so that you have visibility into what's going on with your agents. We're going to look at some agents that are already in production here. So we've got some data that we're collecting and you can see the number of users, we can see the number of sessions, we can see the latency, we can see the quality scores. And once again, there's something new going on. We've got millions of people saying millions of things. How do we even make sense of this data? So we're using AI to automatically tag and categorize the conversation topics that are coming in. So I can dig into a conversation topic, like in this case, connectivity, and then within the conversation topic I can see what worked well and what didn't. And when it doesn't, you need to be able to dig in even deeper. And so this is the Conversation Explorer. And here I can see exactly what happened during this conversation. I can see exactly what was said turn by turn to the agent. I can look at the quality score, I can look at the latency. And here, oh yeah, this is great. He actually clicked on an action here. And when you click on an action, we can see the trace of that action. What actually happened in the system, where was it going? Where was it spending time? Turn by turn. These are the tools that we need to be able to debug to get real visibility into what's happening with our agents. I can see almost everything here, but there's one thing I can't see, there's one thing nobody in this room can see. That's right, the email address of the customer. Because I shouldn't be able to see it because I'm a developer here, Stephan, and I should not have access to that. So let's talk for a moment about data governance. You have seen a lot of data across this demo. You saw sensor data and home data and customer data. You need control of that data and you've got control. Today you've got your data set up, you've identified the sensitive data, you've identified the pii, you've set up your sharing rules. Maybe you're running shield, maybe you're using data mask to mask that data in pre production environments. But we just added a lot more data, we added a lot of unstructured data, we added all of this conversational data. So we need to add new tools to this step of the development process as well. And that's why today we are announcing new data governance tools for Data Cloud. Here we can use AI to set up rules and automatically tag and detect sensitive information across structured and unstructured data. Once we've tagged the data, we have fine grained access controls that allow me to control exactly where, when and how this data is used. So I can set up policies like masking data in, I don't know, we've got some policies here. We're going to redact smart lock PIN codes or obfuscate device serial numbers. And as I set up these policies, I'm in control. I can decide how do I want to mask this data, I can decide when and where. So maybe we shouldn't have access when we're building. Maybe we shouldn't have access when we're testing. Maybe when it reaches production, only some people should have access. My HR department maybe has access. My finance department maybe doesn't. I don't know. It's up to you. So when we go back and look at that chat log, that data masking that you saw there, you're in control of that. So we just saw a quick run through. You're going to see a lot more over the next couple days of how every part of the development lifecycle, building, testing, deploying, observing, all of it is changing. And we are giving you the tools to enable you to do agentic development so you can build your own agents and you can build your own agentic apps on the platform. With that, I'm going to turn it back over to Patrick.

All right, thank you, Alice. So, I mean, again, I just cannot believe how fast this is all moving and how many tools we have at our disposal. Now. When we first started talking about this, I kind of, you know, there's so many customers were like, I don't know if I can get started yet. I don't know if I can trust it. I don't know how to really control it. I mean, here it is, right? If you're in a regulated industry, if you're in a bank or health care, I mean, these are the tools that you need to bring back to your leadership and show them that you can, can do this really, really safely across any type of workflow. So listen, as we see now, we can not just build agents, but we can explore all of the metadata. We can take that metadata and promote it. Well, if we can promote it through orgs, what if we could promote it into some sort of marketplace? We'd all be able to share all of our work together. And so for that I'm excited to welcome. As soon as he fixes his mic, there he is. Christoph, to walk us through how this all comes together in our new agent Exchange.

All right, Thank you so much, Patrick. It's really an amazing time to be developers, admins and architects. I don't know about you, but I think the pace of innovation is just incredible. And to be at the forefront of that innovation, I think that all of us in the room and everyone online, we have an unfair advantage. And that is a strong partner ecosystem that is built on an open and extensible platform. For example, you can integrate any data with our zero copy data partners. You can choose the right model for the job with partners like OpenAI, Entropic, and Google. And of course you can extend the platform with pre built solutions from ISVs. Because as a developer I don't want to reinvent the wheel. I really want to focus on high value features. And together with these incredible ISVs and the community, all of you, we built the leading enterprise app ecosystem in the world. Look at these numbers. Over 7,000 partners, 9,000 listings. And did you know that 91% of Salesforce customers actually use an application from the App Exchange? That is incredible. Every single time there was a shift in the industry, these ISVs built new solutions so that you can innovate faster. Now, as we are facing maybe the biggest shift in the industry ever, we are doing it again for AI agents. That's why today we are announcing Agent Exchange, the trusted Agent Force marketplace built into Salesforce. With Agent Exchange you can deploy Agent Force faster using hundreds of pre built actions, topics and templates. You can extend your own agents with partner actions that you can discover in Agent Builder as you are building your agents. And as a developer, you can package your agents using the AgentForce CLI so that you can then sell it or share it on that marketplace. That is Agent Exchange. It's incredible. It's available today and I want to show it to you in a demo. So let's see the demo. All right, so here we are in the Vivint service application and their agent just escalated that request from a customer. They want to upgrade their cameras and look next best action already identified an offer that's just perfect for them. So the last thing to do is to create an order and then send an updated version of their contract to the customer so that they can sign it. So let's ask Agent Force to do that. So we're going to write a prompt, create an order and send contract to customer to sign. So the agent is reasoning over the available actions and okay, so it was able to create the order, but it looks like it was not able to send the contract. Now why is that? Well, that is simply because that agent doesn't know how to handle an esignature workflow. So what are we going to do? Well, option one, we could reinvent the wheel and build a brand new esignature action from scratch for that agent. Option two, we could use a pre built action from a trusted agent. So what do you think?

I need help.

Option two. Okay, I think we should go with option two. Let's go with option two. And where are we going to find that trusted solution?

Agent Exchange.

Agent Exchange, of course. So let's Check it out. And there it is. Agent Exchange for the first time ever on stage at an event. Look at everything that you have. It's awesome. You have hundreds of pre built actions, topic and templates. It's just incredible. And they're all ready to go. I could install them from here. But guess what, it gets even better because coming soon you will be able to access Agent Exchange directly in Agent Builder as you are building your agent. So Stefan, let's go to Agent Builder and check it out. So here I want to add an action to my agent. So we'll click new and look there is a new Agent Exchange option here. So let's click that and boom, there you go. This is Agent Exchange in the flow of work in the middle of of Agent Builder. Really incredible. Now remember, we want to find an action that can generate a contract and send it around to sign. So let's search for that. We'll search and great. DocuSign came back as a recommended solution. So let's check it out and here I can learn more about that solution. I can even look at the topics and the actions that are available. This looks perfect for what we need. So let's go ahead and install it. Easy peasy. Just going to take a few seconds here and I will be able to see the list of topics that are available and even the list of actions. So let's select all the actions and then add them to the topic. And now we can try again. So we'll ask the agent again to generate a contract for that order and send it to the customer to sign. And again the agent is reasoning over the available actions. Remember there are a few more actions because now we installed DocuSign and now it was able to do it. How did it do it? Well, let's take a quick look at the tracer here. You can see that first it selected the Create contract action from DocuSign.

Great.

And then the Send for E signature action. That makes sense. And that is really the power of Agent Exchange. Extending the capabilities of your agents with powerful actions from partner in the flow of work. That's incredible. But there is one more thing that I want to show you. What if you wanted to create your own agent and then sell it on Agent Exchange. So let's go to VS Code and take a look. So here I'm building an agent and on the left you see a ton of metadata for that agent. The metadata for the agent itself for the actions flow Apex. There is a ton here. And now today I'm excited to share that Using the agent for a cli, you can package all the metadata related to an agent with just a few simple steps. So let's do it. Let's open the command line and here I'm going to use a first command to generate a template for the agent. So that should be quick. There you go. And now I'm going to use a second command to actually generate the second generation package. Remember there is a lot to package here and so it's doing it, it's going to package everything and boom, package created. That is all you need to do to package and share your agents inside your company or on agent exchange. We can't wait to see what you will build. Back to you, Patrick.

All right, we are, we're just about done. We're almost there. Stick with us for a few more minutes because we have our most important part coming up. Now you may remember about 50 minutes ago I said that we want a million agent blazers before the world ends. I have question.

Great news.

The world has not ended. And I have even greater news. My favorite three word phrase in the English language here to talk us through our all new community, Leah McGowan Hare Agent Blazers.

Shout out to all the agent blazers in the house. My gosh, Josh and I were like watching these demos like this. The innovation is innovating. I mean you all are seeing some amazing things. We're talking about agentic apps, we're talking about managing digital labor. Like what technology continues to evolve. The way we work is continuing to evolve. But the question is, are you, are you evolving? Are your skills evolving? Now I don't say that to throw shade or make anybody feel shame. These are questions I have to ask of myself regularly throughout my career. When I started as a mainframe developer and had to evolve into an on prem developer and evolving into cloud computing multi tenant developer and now we're talking about natural language processing development. Like what? This is amazing. And I am so excited at the opportunity to learn new skills. And here's the thing, I know you all are as well because you email me, you DM me, you tell me when you've learned a new trail or you've gotten a new cert. And I love it because it inspires me to continue to learn too. And as we evolve, we have to learn because here's the thing, you all know that the landscape of work is changing and the reality is tomorrow's jobs belong to today's learners. Let me say it again for the folks online camera.

Y' all see me okay?

Tomorrow's jobs belong to today's learners. So it's time to get our learning on. And here's the thing.

We.

We want to help everybody come along. We don't want anybody behind. We're going to bring you all along with us. And we're doing this intentionally. And we've made it easier for you to jumpstart your AI learning journey today with Agent Blazer statuses on Trailhead. The first of its kind, I'll have you know. Yes, you can give it up for that. And just like AI, these statuses have levels. So let's look at the first level. The first level is Champion. This is where you're going to build your fundamentals and you're building your building blocks all around. Data governance with data cloud. And you're going to build your first agent, and you're going to get all of your building blocks. Now, once you've done that, you then unlock. That's right. We don't just give it to you. You got to earn it. You unlock the. The next level, which is Innovator. Now, in Innovator, you're gonna take all of those. Jeez, I see you. You're gonna take all of those building blocks that you've learned in Champion and bring them into Innovator and actually build. You're gonna start looking at use cases, and you're gonna start setting up and customizing AgentForce for service and sales. It's amazing. But now, you ready for this? I don't know. The way you were answering some of Kristof's questions. I don't know. I don't know if you're ready here. Because then once you unlock Innovator, you then go into legend. You gotta say it like that. Everybody say it with me. Legend, Right? This is heavy. This is serious. You really gotta put your elbow grease into this one to earn this. And now listen, all of us are rich enough to pay attention, so pay attention. Here's the thing. You must take the Agent 4 Specialist certification to earn Level 3. That's part of it. So this is some serious leveling, some leveling up. And I really want to make sure you all understand this is so important that all this technology is going to require us to continue to learn. And we're giving you a chance to not just learn, but earn. Now, learning is great, but you know what's even better? Learning together. And we are creating so many opportunities for folks to come together, whether it's one of our Agent Force world tours or our awesome community conferences. You can get together and get Hands on. And really learn together. We are doubling down our investment in this. But what I'm really excited about, I don't know why I'm whispering, but I'm really excited is that we are taking TDX global. Yes, global. We're going to Tokyo, Bangalore, London. We're going around the world. We're gonna bring the learning to you. You are gonna get your learning whether you like it or not. We bringing it to you now. So many ways to learn is building too. And we've had some amazing builders here at TDX take place at a hackathon. And I want to hear more about this hackathon. So, Christoph, you don't mind coming back up and sharing with the folks about the hackathon? That would be.

All right. Well, the hackathon was incredible. We had hundreds of participants, developers, admins, architects. The energy was just incredible. And I know you want to get to the winners, but before we do, before we do, I want to make a quick announcement.

All right, go ahead.

Because I know that all of you, online and globally, you are not able to participate. Participate. So that's why today we are announcing a virtual agent force hackathon. You can sign up today. Here is a short URL. You can all take that URL and sign up today.

All right.

And now let's get to the winners. I need some friends.

Oh, yeah, we got some friends here.

What's up?

And maybe a little. A little drum roll.

Yeah, yeah, a little drum roll.

All right. 2.

The winners.

Awesome. The winners of the TDX 25 Agent Sports Agathon is Team Agent Halo.

Please come on up. You're going to take a picture. All right.

Congratulations.

Congratulations.

All right, well, Joseph, just a few quick questions. Tell us a little bit about your team, where you come from, and how you came together as a team.

We, we actually crisscrossed the country. We came together through the trailblazer community. All of us overlap in the. We're all community group leaders or MVPs Golden Hoodie winners and been involved in the ecosystem that way.

Awesome. And what people really want to know, what did you build?

Our concept was, was an agent network. Eventually, everyone's individual agents at all of these organizations are going to need to talk to one another across the whole value chain. So we took ideas that applied to not just things like on road assistance, with IoT devices, in your car, looking to contact EMS, your insurance company, your tow truck, a body shop, all at one time with one point of reference, but even things that are fun like a book, podcast, and you connect with the author and you buy tickets to their local performance. So there's all kinds of applications across different industries.

That sounds really amazing. And one quick last question. What were you the most surprised by as you were building with AgentForce?

I think the most surprising thing we found about building with agentforce is just how fast things change. I mean, honestly, every time we build something new, there's something new to discover and something new to find about how we can make it do something exciting for us.

Awesome. Well, congratulations again. And Patrick, why don't you take us home.

Congratulations, guys. Enjoy the $100,000. Really appreciate it. Okay, we are all done here today in the keynote room. Download the app, build your agenda. We have a of ton ton of stuff go to true to the core. Join us at the celebration tonight. And one last thing. First of all thank you. Second of all please fill out the survey and tell us how we did. We would appreciate it. All right. Good morning everybody. It.