#285 Raj Koneru: How Kore.ai is Building No Code Enterprise-Grade Agentic AI

Enterprise AI Agents for Work, Service and Process: www.kore.ai https://kore.ai/ Kore.ai https://kore.ai/ founder and CEO Raj Koneru breaks down how enterprises are moving beyond chatbots into agentic AI that actually ships. We get into the no-code tooling behind multi-agent workflows, agentic RAG, guardrails that keep outputs in scope, and why a control layer for governance is now essential. Raj shares real scale numbers, the three Kore.ai product lanes for customer and employee experience, and how partnerships with Microsoft and AWS let teams build where they already run. If you care about building secure, explainable AI agents that integrate fast and scale cleanly, this one is for you. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI (00:00) Raj Koneru's Journey & The Birth of Kore.ai (03:10) From Chatbots to Enterprise-Grade Agents (06:33) Security, Scale & Proof in the Market (07:04) What Agentic AI Really Means (12:16) Building & Governing AI Agents (17:26) Kore.ai's Product Lines & Differentiation (20:22) Industry Applications & Case Studies (28:17) User Experience & Change Management (34:46) Governance, Identity & Cost Controls (39:56) Adoption Timelines & Market Outlook (43:51) Roadmap & Partnerships (47:38) Future of the Enterprise AI Landscape

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  It's the DNA of our company, my DNA. We're most comfortable building a platform that requires the security and scalability that an enterprise requires. And we know how to sell it to an enterprise. We know how to support an enterprise. The complexity, sophistication that an enterprise needs is not something that everybody can do. At the end of the day, the breadth and the depth of the platform that we have now comes from our experience from deploying this for ten years. So we have about a thousand people in the company and six fifty of them are in R and D and product development. So as you go about building your AI agent, all the bells and whistles that we have seen across hundreds and thousands of implementations, we've built into the platform.

• 0:40Speaker 00

  My name is Raj Kore, I'm the founder and CEO of Core AI. So I started this company eleven years ago in twenty fourteen. And prior to that, I built a company called Koni, which was an enterprise mobile platform company. And prior to that, I built three different services companies, IT services companies, a couple of them that went public and all three of them eventually got sold. And Koni got sold about six years ago. So that's how I got to Core AI and has been building Core since then, essentially.

• 1:18Craig S. Smith?

  What's the premise of Core AI and and how is it related to coding?

• 1:24Speaker 00

  Well, if you think about the progression of technology, you know, from mainframe screens to Windows applications to then web applications.

• 1:39Speaker 00

  You know, I built a company every time there was a transformation and I have this knack of anticipating that new technology before it hits and investing it ahead of time. So Koni, I started in two thousand and seven when the iPhone didn't even exist. And the time was clear to me that.

• 2:06Speaker 00

  Users are going to use a smartphone to be able to access data and do transactions and consume information, while all of us were used to doing that on the web or through Windows applications. So I realized that enterprises would have to build out these mobile applications and to do that they need a platform and a platform that enabled you to build once and deploy to multiple smartphone systems. That was the premise of Koni. We became the number one in the Gartner Magic Quadrant. We became the largest in the enterprise mobile platform space.

• 2:44Speaker 00

  So as users went from clicking a mouse, clicking on menus and navigating through screens on the web, you know, mobile brought the touch interface, which, you know, initially with the BlackBerry type of interface and then eventually with the Android and iPhone type interfaces. And that was a different way of interacting with data and information. And while I was doing that, you know, there was some articles I read about natural language processing and how the computer can understand natural language.

• 3:23Speaker 00

  So I got interested in that and started imagining that users can, instead of touching or navigating through screens or menus, can just speak in natural language. And if the computer can understand what you meant, it can then collect the data that you need and present it to you. It was way ahead of its time. I mean, at that time, the modern day machine learning models were just coming out, open source or otherwise. And as we started building out a platform for natural language processing and building

• 4:07Speaker 00

  what used to be called chatbots and voicebots, we realized we had to build the models ourselves for NLP. So we used some of the open source machine language models, but then we built out our own semantic model and a knowledge graph model and created this framework without writing code for people to be able to build out chatbots and then voicebots.

• 4:34Speaker 00

  The problem was that going back to twenty fifteen. Um enterprises and their I T teams really didn't understand. Uh language computing. You know, they were all schooled and experienced in U I computing, but not language computing. So we had to evangelize the space. There were very few players, us and Google and a few others. And then eventually twenty eighteen onwards, things started taking off. Where specifically for a few use cases like automating customer service. Or I automating employee service around I T workflows, H R workflows makes sense.

• 5:19Speaker 00

  And we started growing pretty rapidly. Uh and then of course, you know, two and a half years ago, um chat G P T hit the market. And L L M set the market. So by then we became the number one leader in the Gartner Magic Quadrant for two years in a row. Uh we became the Forrester Wave leader, the number one leader. And we had acquired like many hundreds of customers, large enterprise customers. And the platform, while you could create a chatbot and a voice bot without writing code. And deploy that on, let's say, I V R or any digital channel.

• 5:58Speaker 00

  Uh like web and mobile and inside messaging apps like Slack and WhatsApp and whatever. And our customers were deploying that at very large scale, like a large bank, we process three hundred million calls a year. Uh a larger uh health insurance provider, they process three hundred million calls. Uh at the time, a large telco, again similar number. Uh the largest, one of the largest e-commerce providers, we process like four hundred million chats and calls. To do that, security and scalability was key. So over the last ten years, we built this foundation.

• 6:42Speaker 00

  Where we could guarantee the security of the back and forth between the users and enterprise systems and encrypt everything and make it only visible to the enterprise. And also from a scalability standpoint, nobody built a platform that was scalable with these volumes. But when the L L M's came out, we had to change up the framework by which you can build and deploy. But the foundation of security and scalability still is the same. So by changing that up, we've now made it an agentic AI platform where you can choose when you build in what's called an AI agent now. What used to be called chatbot and voice bot, the same thing.

• 7:28Speaker 00

  But with an AI agent, you could choose the level of autonomous behavior versus prescriptive deterministic behavior. So now we have this broad platform where you could build something completely deterministic, like customers used to bring before. Or completely autonomous with multi-agent orchestration, which is the latest, you know, fad, if you will. With completely agentic or partially deterministic and partially autonomous. With only one who can do that, with that security and scalability and governance that has always been our bread and butter. So that's where, that's how the things have transformed.

• 8:13Speaker 00

  And the purpose of why we started as a company, which is natural language, input. Computer understands it, delivers an output, and then you continue the conversation. Is now truer now with L L M's being able to do planning and reasoning and understanding than before their L L M's.

• 8:34Craig S. Smith?

  Yeah.

• 8:37Craig S. Smith?

  Excuse me. So when you say agents, you're talking about conversational agents, not agents that will use tools or perform other tasks in the in the real world.

• 8:59Speaker 00

  Both, both.

• 9:00Craig S. Smith?

  Both, okay.

• 9:01Speaker 00

  Yeah, so you could have a conversational agent where on one end you have a human,

• 9:09Speaker 00

  you know, initiating the conversation and the AI agent responding in context. And on the on the other hand, you could have an agent that gets triggered either by a human or by an event in a system and then goes through a series of steps using multiple LLMs and multiple prompts to process data and complete complete its action. So that's not so that kind of an agent is what we call asynchronous agentic workflows. So you could build either one on a platform.

• 9:42Craig S. Smith?

  I see. And the platform is specifically for enterprises, is that right? Is there a reason why you don't have a consumer

• 9:54Craig S. Smith?

  consumer grade product? Because, you know, agent building is now for everybody, not not just enterprises.

• 10:03Speaker 00

  Yeah, I mean, it's the DNA of our company, my DNA, right? I mean, we're most comfortable building a platform that requires the security and scalability that an enterprise requires, basically. And we know how to sell it to an enterprise. We know how to support an enterprise. The complexity sophistication that an enterprise needs is not something that everybody can do. Whereas a consumer based, you know, uh, something that does something like, you know, book you flight tickets or books you, uh, schedules an appointment, anybody can do because the level of sophistication and security and scalability is much, much less of an issue

• 10:43Speaker 00

  in the consumer world. And, you know, at the end of the day, the platform can be used by consumer developers, basically. But our focus has been on enterprise, which, which is where our comforts are, labs.

• 10:57Craig S. Smith?

  Yeah. Uh, and, and so for enterprises, uh, uh, that, that are, this is a sort of self-serve, they go on and there's, uh, they, with a login or whatever level of, uh, of subscription you have, uh, you can, uh, mix and match and, and build using, uh, conversation is in the background. Is there coding going on or, or are you assembling pre-built, uh, components?

• 11:34Speaker 00

  Yeah. I mean, if the, uh, enterprise can self-serve, they have to learn our platform. So we have something called code.ai Academy, which is like an online academy with about 250 courses, self-learning courses, or they can call us and we can have somebody teach them. Many of them tend to use our partners, our SI partners. We have like six hundred of them from the large ones like Capgemini and Deloitte and TCS and people like that to smaller local partners who have built out practices in our technology, basically.

• 12:11Speaker 00

  And it's, there's no coding involved, zero code, basically. So you build your AI agent conceptually in your mind and then use the no-code tooling to be able to, uh, put it together. And then there's the whole testing framework to test your AI agent. There's an evaluation studio to evaluate, uh, what the AI agent is doing every step of the way and why the LLM provided the response that it did and what context was passed to the LLM. So there's the whole traceability, explainability, observability built in. So once you build it. Then you can test it, you can evaluate it as you're using it and then be able to deploy it.

• 12:54Speaker 00

  Basically at large scale on any of the clouds, we're cloud agnostic, so we run on all the clouds. Some customers even want to run us within their own data center, inside their own file, inside their own firewalls and uh many customers do that as well.

• 13:10Craig S. Smith?

  Yeah, uh but uh under the hood, I mean, if I go on and I using the conversational interface, say, uh, you know, I want to build an agent that will uh uh process expense reports, right? Uh, is there, you you say that conversationally on the on the back end, what the user doesn't see, but is there, uh, is it like cloud code that's encoding up an agent or are there components that that uh that, you know, you, well, we have this

• 13:54Craig S. Smith?

  this rag uh component and we have, uh,

• 13:59Speaker 00

  Yeah.

• 13:59Craig S. Smith?

  you know, whatever it is and that that that stitches them together.

• 14:05Speaker 00

  Yeah, so it's more component based. So there's an agentic rag engine behind, there's a framework behind that will take your prompt and then string it together with your next prompt, basically. You can do conditional branching, uh, there's tool calling, there's M C M C P clients that we built in. So you as a builder, you give the instructions, you know, through through our no code tooling. And that then constructs in the back end a workflow, basically.

• 14:41Craig S. Smith?

  Yeah.

• 14:41Speaker 00

  Which which then it gets executed, it's not code generation per se, basically. And then you can feed in your documents or have have the agentic rack system extract content from different websites. And then use a uh using a word embedding model, basically it would do the ingestion basically into the vector database. And then there is a predefined retrieval pipeline, which you can also customize, basically for your specific needs. So the combination of the workflows that you create with the Agentic right together makes up the AI agent.

• 15:20Craig S. Smith?

  Yeah, you know, I've seen, I've been talking to a lot of people about agent building platforms. And and there are a lot of them now. How do you guys differentiate yourselves? I mean, I understand that you're in a little ahead of a lot of people, but yeah.

• 15:40Speaker 00

  Look, at the end of the day, the breadth and the depth of the platform that we have now comes from our experience from being from deploying this for 10 years. So we have about 1,000 people in the company and 650 of them are in R and D and product development. So as you go about building your AI agent, all the bells and whistles that we have seen across hundreds and thousands of implementations, we've built into the platform, whereas others only started to build a platform, like an agent, agent building platform in the last year or two. So they're going to have to learn that experience over time, whereas we are the most experienced in this, where we've deployed it scale.

• 16:23Speaker 00

  Secondly, there's no code. We have a no-code platform. Thirdly, unlike some of the others, they are not cloud agnostic. They are the cloud providers themselves, basically. Or they're a data provider like a Snowflake or a Databreak. And then they're tied to the data source, we're data agnostic. And we're model agnostic. We support like pretty much every model on the planet and we also support bring your own model, essentially. We also have a model factory and a model studio, basically where you can create your own enterprise data set and generate synthetic data from it to create a complete, complete coverage, golden data set,

• 17:06Speaker 00

  bring an open source model and fine tune it on our platform and host it on our platform as an API. So the breadth is all of that. The evaluation studio, the data studio, the model factory, the model hub, the no-code tooling, agentic rad, that's the breadth, basically. The depth is for the use cases. So we go to market with primarily three offerings. One is called AI for Service, which is three sixty degree customer service. The customer comes in, meets an AI agent who tries to automate that interaction. And then we integrate with every C K I system out there. So the customer ends up with a human. The same AI agent starts helping the human agent in real time at listening to the customer in real time.

• 17:52Speaker 00

  And then once the interaction is over, we measure the interaction between the customer and the AI agent and the customer and the human agent to find opportunities for improvement and then feed that back into the AI agent and feed that back into the human agent. So that's AI for Service. Then we have AI for Work, which is employee experience. It's rooted in enterprise search where we have over hundred connectors which are optimized for agentic rad. You can build your own connector without writing code. You can build five types of AI agents like a prompt agent, workflow, agentic workflow agent, multi-agent app, a conversational AI agent, basically.

• 18:34Speaker 00

  And to be able to create these AI agents by the employees themselves or by pro developers for the employees. And finally, we have pre-built agents for I T integrated to ServiceNow and B M C, H R integrated to Workday and SuccessFactors and Oracle Recruiting integrated to applicant tracking systems that comes along with AI for Work. Similarly, for AI for Service, we have AI for Banking, AI for Retail, AI for Healthcare, which are pre-built agents so that you don't have to start from scratch. And lastly, we have AI for process, which is what you alluded to earlier, which is the process automation component, where it's not conversational,

• 19:18Speaker 00

  but there's a process designer and a process monitoring and management tools that are on top of the same platform. So it's the same platform for all three offerings with three different, you know, entry points for three different use cases. And we're the only company that gives you that breadth of the use cases, but basically with the depth of the depth and breadth of the platform underneath. So Gartner came out with the their emerging Gen AI leaders quadrant like last week. And we were rated almost at the very top for Gen AI Engineering, which is the platform. And for knowledge and productivity, also we're rated at the very top.

• 19:59Craig S. Smith?

  Yeah, well, congratulations on that. The uh I just had

• 20:08Craig S. Smith?

  the CEO of Boomi on. Do you know Boomi?

• 20:12Speaker 00

  Yeah, the integration company.

• 20:14Craig S. Smith?

  Yeah, do you do you guys use something like that? I mean, you mentioned M C P.

• 20:22Speaker 00

  Well, I mean, M C P is so new, but our customers use Boomi. You know, build their AI agents on our platform and then integrate it to services available and exposed by Boomi. Basically, as they do with other other competitors of Boomi as well.

• 20:39Craig S. Smith?

  Right, right. And are you dominant in one vertical as opposed to another?

• 20:48Speaker 00

  No, financial services is about thirty five percent of our revenue. Healthcare is about twenty percent. Telecom and technology is about twenty percent. And then the rest is retail, consumer, travel and some B two B. So we tend to focus more on. Large consumer facing verticals globally. Uh seventy percent of our business is in the U S. And the remaining thirty percent is in Europe and Asia. So we have global of a global customer base across different verticals, but. Not as much in government or B two B sectors, more in B two C kind of sectors.

• 21:27Craig S. Smith?

  Yeah, uh and and um for example, in financial. Uh is wh- what's the the largest implementation? Is it sort of back office stuff? Uh, yeah.

• 21:44Speaker 00

  No, front office customer service is the number one use case for for AI. In an enterprise, so um the ROI is easy to determine. You reduce the cost of service. That's what the ROI is all about, whether you reduce the number of human agents or you reduce the time a human agent. Takes to satisfy a customer, all of that. Basically, so this is with the number four bank in the U S. Uh we process three hundred million calls for them and chats. And that is our largest customer. But they've also deployed employee facing use cases on the same platform. For I T, for developers, for DevOps engineers, and they build all those on their own.

• 22:29Speaker 00

  They have over a hundred AI agents. But the one AI agent that has the most volume is a customer facing uh AI agent.

• 22:37Craig S. Smith?

  Right, uh and that's for helping customers navigate. Uh how to do various things uh.

• 22:47Speaker 00

  Yeah, you know, check your balance, transfers, payments, apply for a loan. Understand how you're spending your money, that's like two hundred workflows. Basically, and also asking questions, how do I apply for my loan for this loan? Uh am I qualified, those kinds of things, anything you would ask a bank. Basically, whether you are, whether you have your bank account with them or have credit cards with them or investment accounts with them.

• 23:14Craig S. Smith?

  Yeah, how how do you um?

• 23:18Craig S. Smith?

  Fence off an agent from, I mean, I've, I've built a couple of conversational agents for specific things, but if you ask them, because their underlying models are, you know, the big general agents foundational models. Yeah. And, and so if you build something specifically for travel, but you ask them, you know, who was Copernicus? It'll answer that. It, you know, and then you can, just as you can with any of the foundational models go off on a

• 24:03Craig S. Smith?

  on a tangent. How do you, or, or do your agents still have that capacity? But they're just fine tuned in a specific, for a specific use case and they're tied to a knowledge base for that specific use case. But if you, if you talk to it about something unrelated, it'll, it'll answer.

• 24:28Speaker 00

  Yeah. So there's, you could do both. You could take an open source or a foundational model, fine tune it with your enterprise data by creating a golden data set. And then it's very limited in terms of its scope. This again, some of our customers have done that. But most customers don't do that anymore, basically, right? Because then they'll have to maintain the fine tune model, worry about its accuracy, latency, all of that thing. So the way to do that is to follow. Okay. And the way we do it is using these two methods. One is guardrails. So we have a guardrail framework within our platform.

• 25:10Speaker 00

  There's a bunch of prebuilt guardrails around bias and gender and things like that, which you can use. But you can build your own guardrail, basically. So when the input comes from the user, you can apply one or more guardrails to see whether to let the input through to the L L N. And when the response comes back from the L L N, again, you can apply one or more guardrails to see if the response is within the parameters that you want it to be. Okay. And most of our customers use the guardrail framework and the guardrails that we provide or they built, you know, to avoid going out of band. The other one is context.

• 25:48Speaker 00

  So when you give the, when the prompt is generated by a platform, we, we also attach context, basically. And in the context and in the instruction to the L L M, we generate specific language to, to tell the instructor L L M that only stay within the scope of these topics. If the input comes, you know, related to, unrelated to these topics, you know, just say that that's out of my scope, essentially. So by doing that, but you can't explicitly expect the developer to provide that, you have to implicitly generate that,

• 26:29Speaker 00

  basically. And that's what a platform does. So no L L M is hundred percent going to, you know, follow every instruction. But the quality of the prompt, basically, and the quality of the positive and the negative outcomes that you want, that get generated, you know, restricts the L L M to, you know, what you want it to generate. And then there's a third way of managing that, which is, okay, whatever the response the L L M gave, you through, you run it through another L L M to determine whether it was staying within the topic, topics that your AI agent is supposed to answer to. And if it doesn't, then it doesn't send

• 27:10Speaker 00

  the response to the user. It regenerates the response from the first L L M until it, until it's sure that it's remaining within that topic. There's some latency and cost to that, but it ensures that you remain within scope.

• 27:26Craig S. Smith?

  In that loop, um, with a, a second L L M, that's, um, is that a, a choice that the user makes?

• 27:37Speaker 00

  Yeah.

• 27:39Craig S. Smith?

  Yeah.

• 27:40Speaker 00

  You have guardrails. That's the easiest one. The second is the generated context. Basically. And the third one is, you know, run it through a loop through another land. The third one is a more expensive. Option. The first two are the easier options. Basically, so most of the time, you don't need to use a second L L M call to be able to stay within context.

• 28:02Craig S. Smith?

  But that second L L M call, if you want that, is it? Again, you're the user.

• 28:08Speaker 00

  Choice.

• 28:09Craig S. Smith?

  With the conversational interface or their dropdown menus or? Or how how do you make that choice? It's.

• 28:18Speaker 00

  I mean, this is the choice that the developer create makes. You know, to avoid the L L M answering out of out of bank.

• 28:26Craig S. Smith?

  Yeah.

• 28:27Speaker 00

  The user, the user interface, the input could be. Voice could be text. It could be. Uh, they could have uh, upload images, upload videos. And if they did, we use a multimodal, you know, airline to process that. Basically, the output. The output again can be text or we provide within our S D K. Many U I components to take the text that was received and show it in a table, show it in a pie chart. However, you want to display. Basically, or send a link to a video or an audio and be able to show that to the user. So that is again, something that's available to the developer to create that experience.

• 29:07Craig S. Smith?

  Yeah, I see. Uh, but yeah, and what I was asking, I guess, when I said user, I meant developer. Uh, how does the developer? Tell uh.

• 29:20Craig S. Smith?

  Core A I, the the. Platform, the the platforms called uh.

• 29:26Speaker 00

  The exit platform.

• 29:28Craig S. Smith?

  Is or what's Gale?

• 29:31Speaker 00

  Oh, Gail was an early version of the agent platform.

• 29:34Craig S. Smith?

  Okay.

• 29:35Speaker 00

  That was for asynchronous workflows, but now we pulled that into the agent platform as well.

• 29:41Craig S. Smith?

  Okay, but the developer, when he's working on the agent platform, is his interaction all through conversational AI? Or are there buttons and dropdowns and drag and drops and things like that?

• 29:57Speaker 00

  All of them.

• 29:58Craig S. Smith?

  I see.

• 29:58Speaker 00

  So some of it is conversational, some of it is drag and drop to create a workflow. Some of it is, you know, making choices in your right-hand side panel, in the property panel as you're building it, basically. So it's all visual or conversational. It's never, never, I mean, if you want to write code, we provide abilities for you to write code, to manipulate data or whatever, but you don't need to.

• 30:25Craig S. Smith?

  Yeah. Yeah. So you've been in this longer than a lot of the, the people that are

• 30:36Craig S. Smith?

  offering agent building platforms now.

• 30:41Craig S. Smith?

  A lot of people are talking about, you know, as the agents climb the complexity ladder, that, that you'll eventually have swarms of agents. I mean, I, you know, we're both paying attention to the same space. And, and you'll have

• 31:05Craig S. Smith?

  managers who sort of manage their group of agents. But the Steve Lucas who from Booming, who I just had on, he was talking about a headless organization. You know, the someday, that's what organizations, organizations will be. There will be one guy that handles all the customer facing agents and other guy who handles all the business process agents. And, and there won't necessarily be a CEO managing all of the managers.

• 31:50Craig S. Smith?

  I mean, where do you see this going?

• 31:54Speaker 00

  You know, that kind of a view is a very utopian view in my mind. Basically, the practical view of it is this is no different than any other application. You know, when you first got a mobile application from the enterprise, you know, you learnt how to use it and you figured out, you know, what its value is and how, you know, versus a web application versus something else. An AI agent, you know, there's a lot of hype and stuff about it, but it's an application. A human gives it input or it gets triggered, it does a job and it comes back with a response. So either a human is a consumer or another set of applications are consumer, basically.

• 32:39Speaker 00

  The only difference here is that because of the planning and reasoning capabilities of the L L M's, basically, a lot of that and analysis and reasoning that we would do in our mind by looking at data, some of it it will do for you and give you the final result, basically. So for example, you wanted to write an email, right, in L L M, you just give it the broad context of what it wanted to write and it does the hard work of actually writing it for you. But then you review it and say it's okay or not and then you go forward. Similarly, any application that you can build as an AI agent would do something similar. So my opinion is, look, people run businesses, basically,

• 33:20Speaker 00

  and people provide services and goods to other people, maybe our human society. These applications help us to be more efficient, basically. So there's going to be swarms of these at the enterprise, much like there are swarms of applications today. Maybe more AI agents than we had UI applications before. The issue is not that, the issue is with these things is there's a new. Complexity with these things and the complexity is not in building them or deploying them. It's then, how do you, how do people use them? So there's a change management element. When we first introduced mobile applications, employees were not comfortable with it.

• 34:03Speaker 00

  Or when web applications came out, but over time they got comfortable with it, right? Same thing with these, that's number one. Number two, the governance and control of these things. These things can discover each other and trigger each other. They need to have identity, they need to have discoverability. They need to have, an agent needs to have access to data. So what access, what data can they have access to and what data they cannot have access to? Because with MCP and tool calling, you know, they could just call any tool and go execute something. So restricting what they have access to is three. Which humans have access to these AI agencies for, basically?

• 34:45Speaker 00

  And number, number five, most important, regardless of the AI agent being built on my framework, on my platform, or built on some other framework or platform. You know, while there may be communication through A to A and tool calling using MCP, basically. You still need observability and explainability across the set of AI agents, no matter who built them and how they were built. So there is a need for a higher level governance layer, what we call a control layer, that each enterprise needs to have. So that headless CEO is that governance and control layer, basically. But you're still going to have a human, you know, looking at that governance and control layer

• 35:30Speaker 00

  to ensure the army of AI agents are behaving properly, basically. So it's the same thing like, you know, you have admins for UI applications and there's a central admin. You just need, in this case, a lot more governance because there's cost involved, there's L L N cost involved, there's compute resources, GPU resources that are being used, discoverability that's coming to play, basically. All these things, and so our platform, while you can build. Great AI agents, you can deploy them at scale securely, which is our bread and butter. You know, we're implementing that control layer. So at the central layer, there's a control and governance layer, which is agnostic of which AI agents were built by whom

• 36:14Speaker 00

  and deployed where, using which models and connected to whichever data. And we uniquely can provide that value proposition to an enterprise because we're not tied to anything at the bottom layer. We're not tied to a model, to a cloud, to a data, where there is, you know, a preference to keep my legacy alive. You know, we are the modern platform where everything can be, you know, used and consumed, but with the control that can be on top.

• 36:42Craig S. Smith?

  Yeah, and so, you know, you mentioned, or I mentioned swarms.

• 36:52Craig S. Smith?

  And, you know, I'm having these conversations with people like you, with researchers who are, you know, looking at the world from the point of view, the, the, the, the leading edge of this stuff. But it really hasn't penetrated the enterprise at scale yet. I mean, people are still playing around. It's mostly customer service agents or conversational interfaces.

• 37:27Craig S. Smith?

  How quickly do you see this progressing to, to where agents are really all over the place?

• 37:41Speaker 00

  I mean, think about it, Craig. Did you hear the word agentic less than 12 months ago before? I mean, did you hear the term MCP?

• 37:50Craig S. Smith?

  I, I remember the first time I heard it, yeah.

• 37:54Speaker 00

  So, you know, we are all anxious people, right? You know, it's there now. Why haven't, why, why isn't everybody using it yet, basically, right? Agentic is new, MCP is new, A2A is new. There's more standards and protocols coming, essentially. Models are moving fast in different directions, multimodal, more reasoning, research models.

• 38:21Speaker 00

  Education and enablement for an enterprise takes time, basically. So it's just the natural, you know, ebb and flow of things in an enterprise. You know, enterprises, as soon as the web and the internet was there, they didn't immediately turn around and change everything up in their organization, right? It took ten years, basically. This may not take that long. But I think that adoption cycle is early, basically. They're also trying to sift through the biases a bit question. They're trying to sift through all the noise that's there and all the hype. So my thing is, look, whatever success is there is great. You know, and more success will build on that success.

• 39:05Speaker 00

  And adoption will grow and it will start growing much faster in the next two or three years, basically. So I think we'll get ahead of us keys in terms of expecting more value to be generated, more AI agents to be deployed out of that. I think it's all coming. We just have to be a little patient because we're talking about large enterprises that have been around for many, sometimes hundreds, hundreds of years with SOPs and processes and people who have to understand this, that update, change management needs to be put in place to get it going.

• 39:40Craig S. Smith?

  Yeah, and to that point, isn't there an opportunity for a GenTek native companies to leapfrog in various industries?

• 39:55Speaker 00

  Sure, you know, the web opportunity created Google and eBay and

• 40:00Craig S. Smith?

  Yeah.

• 40:01Speaker 00

  the mobile opportunity created Uber and, you know, all the delivery companies and everybody, right? So absolutely, I think there's going to be some runaway AI native consumer companies that will come about. I don't think anything has come yet. It's still all rooted in the chat GPT equivalent, like perplexity or whatever. But certain industries will have some upstart that will turn that industry, disrupt that industry. I suspect healthcare will be one of them, maybe travel, maybe financial services or maybe all of them.

• 40:43Speaker 00

  But that's also ahead of us. Basically, I'm sure they're being conceived right now.

• 40:49Craig S. Smith?

  Yeah, uh, it's you have something called XO Express or are my notes old?

• 40:56Speaker 00

  That's old.

• 40:57Craig S. Smith?

  Okay.

• 40:58Speaker 00

  XO was the name of the platform XO Express was the SMB equal and PLG type of thing.

• 41:07Craig S. Smith?

  Right.

• 41:07Speaker 00

  When we, when we moved to Agentic, we put a stop to all that and said, look, Agentic is, that things are going, we need to get that right. And we go back at an express offering in the future.

• 41:18Craig S. Smith?

  Right, so there isn't a small business uh tier uh right now.

• 41:28Speaker 00

  Not yet. Again, we're in that education cycle. We need to get the market to a point where small businesses are able to turn up. I think getting the enterprises to turn up is the first tier of opportunity.

• 41:42Craig S. Smith?

  Yeah, and the business leaders, uh, is they're looking at all of this and trying to decide um who, whose framework or platform to adopt, uh, what advice do you have for them other than to use Core?

• 42:02Speaker 00

  Define a P O C?

• 42:04Craig S. Smith?

  Yeah, if, if, if, if you're the, the

• 42:10Craig S. Smith?

  the CTO or

• 42:12Speaker 00

  Yeah, I mean, look.

• 42:14Craig S. Smith?

  Yeah.

• 42:15Speaker 00

  Define, it's very simple, actually. It's not that complex. Uh, you don't need to go hire an Accenture or a M and Z to go figure it out, basically. Um, most CTOs or most chief AI officers have already done, done surveys with their companies to determine from each department, what are the potential use cases? And pick two or three of the simple to complex use cases and define a P O C and create a success criteria. Basically, and then go pick the top five vendors that you know of, like Dr. Gartner, Dr. Forrester, who figured out who the top five are, or if you have existing vendors who you feel comfortable that they may have the chops to do it.

• 43:00Speaker 00

  Get them to execute those P O Cs, basically, and see the results. That will give you the best, you know, look at what is good and what is BS, if you will.

• 43:11Craig S. Smith?

  Yeah, yeah, and uh, do you have, do most of the?

• 43:17Craig S. Smith?

  Frameworks have a

• 43:22Craig S. Smith?

  trial period of free trial, or I guess if you're a big enterprise, it's not that important.

• 43:29Speaker 00

  Well, I mean, most everybody will give you a free trial, maybe not. You know, the very big ones, but you know, most innovative Gen AI thing will at least do a P O C if not give you a free trial in production.

• 43:44Craig S. Smith?

  Yeah. So what what's next on the core AI roadmap?

• 43:51Speaker 00

  Lot of things. So as the AI landscape changes with the models and and all these protocols that are coming in, you know, there's things like adaptive memory that we're building. There's this whole optimization of the AI agent by using different L L M's for different parts of the AI agent that can dynamically be determined. This whole control panel that I talked to you about is under the big one.

• 44:20Speaker 00

  There's things in AI for work that we're doing, which adds more capabilities in the employee experience area. There's a wrong roadmap in AI for process, for process automation. We are already the leader in AI for service, basically. So that's something that we will continue to do. But we also have a lot of pre-built agents. So we have a marketplace of about two fifty AI agents that anybody can access free of cost and go and deploy on our platform.

• 44:51Speaker 00

  We're going to build more as we go forward, but we're enabling our partners to build on our platform and deploy on our marketplace. But also, most importantly, we're doing a co-build with our strategic partnerships that we've signed with Microsoft and A W S. We're tying our agent platform with their front-end agent platforms like Bedrock and AI Foundry. We're tying our AI for service with their service offerings around customer service. And AI for work with Copilot from Microsoft and Q Index from Amazon. And AI for process with Power Automated at Microsoft and and Q workflows with Amazon.

• 45:31Speaker 00

  So mostly, we've grown a company organically, going directly to customers and engaging with them and and servicing that. Now to get scale, we've

• 45:42Speaker 00

  Committed to the Scobay and co-market and co-sell and we're very excited about that. You know, they have, they have been great in our conversations with them. They recognize us as a leader in the space and they want to win together rather than win alone. So we've created these better together stories which start, which have already started resonating with many customers.

• 46:05Craig S. Smith?

  And, and is that on, for example, with Bedrock on, on if you're on Core AI that you can use Bedrock resources or you can build on Bedrock through Core AI? Or is it the other way around if you're building with Bedrock? What's that?

• 46:29Speaker 00

  It's both. I mean, you can build with Core AI and consume Bedrock resources and run on Bedrock. Basically, and consume AWS cloud resources. Or you can build on Bedrock and use components of the Core AI platform and still deploy, test and deploy on Core, but run on Bedrock. So we've brought both the platforms together very intricately. So that no matter where you start, it looks like one to the developer. Same thing with Microsoft.

• 47:04Craig S. Smith?

  Right. And yeah, same thing with Microsoft. Yeah. Are there any other, are you doing anything with the other guys with Google?

• 47:15Speaker 00

  Yeah, we're, we're, we're talking to them actively right now. We'll announce one like sometime in the next few months. But Amazon and Microsoft were announced at our customer conference about, about three months ago. And where, I mean, a large part of my company is like stuck on those two motions right now.

• 47:38Craig S. Smith?

  Yeah, yeah. Oh, that's fascinating. Who, who, how do you see the, the market developing? I mean, as I said, a lot of people are getting into the space. Do you think that there's room for everybody? And people will become dominant maybe in one vertical as opposed to another? Or do you think there'll be a period of consolidation where a lot of the newcomers maybe have a feature that others don't or get? Get acquired for that feature and you'll end up five global players in the agentic platform space.

• 48:20Speaker 00

  Yeah, and look, I have the benefit of being around for eleven years. So if you go back and read, I've gotten a report from, I think, twenty nineteen or twenty twenty. They, the starting line in that report is that are over two thousand vendors in the space. And no leader, and here they picked like twenty five companies that are interesting and they didn't put out a Dr. Magic Quadrant. Two years later, they put out a magic quadrant, we came out number one and there were like about fifteen other companies below us. And then the following year, again, they became number one. And when you look at the leaders quadrant within that magic quadrant, that was about four or five.

• 49:01Speaker 00

  So back again, there's probably two thousand plus vendors now and confused market and confused customers. And the men will be separated from the boys in the next two or three years. So there'll be like, in my opinion, the top four or five, which will gain large market share. And then there will always be a tale of vendors who have smaller market share, some will die, some will get acquired and whatever happens to them. I'm talking purely about the agentic platform space. And there are going to be vertical AI agents. I'm a company that provides this custom-built AI agent, you know, pre-built AI agent for banking.

• 49:42Speaker 00

  Or something for HR or something for IT, but you have very limited use of that. So if you're an enterprise, you've got to be asking yourself, if there are going to be swarms of these AI agents. Do I want twenty different technologies in, on which these things are built? That my teams have to learn and maintain all of that, or do I want to standardize on one primary platform? And maybe I buy something from Salesforce that's pre-built and serviced now, but what I build and deploy myself. Do I put it on one platform? So I have sanity to this. And that's the question that we're posing to customers, saying, look, you can encourage everybody, you know, buy from everybody.

• 50:27Speaker 00

  Guess who's going to like deal with all that? You. Basically, or you can do your evaluation properly, pick a platform. We have a lot of history in maintaining such a platform for customers than others do. So make your bet, basically. It's you're not going to build everything in day one. You know, as you go forward, you know, it enables you to do this in a much more so much more sane manner than creating insanity for yourself.

• 50:55Craig S. Smith?

  And we're in that process now, right? Where uh big enterprises are making their bets and it seems that if you're already uh large and established, you have something of a of a of an advantage because.

• 51:12Speaker 00

  Yeah, so we have over five hundred customers deployed at scale. All these are large global two thousand companies. So we're there already. And as they go through their transformation from pre-agentic to agentic, you know, they've already bet on us. And we're adding the next five hundred on our own with help from Microsoft and AWS. So we are on a way to be one of those men separated from the boys.

• 51:38Craig S. Smith?

  Okay, is there anything I haven't asked that you want listeners to hear?

• 51:43Speaker 00

  No, I mean, look, I think, like I said, uh when I mature AI company and there are very few of that kind in the market. And for enterprises, you know, doing their diligence and separating the noise from the the worthy stuff is super important at this point.