There's a recent report that came out from Gartner that says by 2030, 90% of IO heads will use hybrid automation the way that we're talking about it. That's up from 10% in 2026. So it's not if, it's when. I think everybody's predicting that this is gonna come together. Everybody's predicting that, agentic steps or agentic systems will be used in operations. It's gonna take time. And what we're seeing are actual customers coming to us and doing this today, but it's their early adopters. And that's ex... Exactly what we expect to see at this stage. To make it super clear what we're doing here and how this is progressing inside of Universal Automation Center and other SOAP applications as they mature too. If you look at this first layer, this is really where we sit today. This is your deterministic automation. There's no agentic steps. It's very repeatable, and there's no probabilistic AI inside of it. The next stage and the one that we see the most of right now is where they're adding an LLM task or better said, they're calling an LLM model to do some reasoning or thought and then passing the information back into the deterministic workflow. We'll talk a little bit more about these patterns in a moment. The next step, which is where we think everything is heading, is this agentic orchestration workflow where you have some deterministic steps. You may call an LLM model to make a decision or synthesize data or analyze text. It'll pass that unstructured data back as maybe structured data and send it to an agentic task, which splits off and runs its agentic agents in the agentic orchestration platform, then it'll send that information back to the workflow and maybe call another agent. But the real future workflow, or even some current workflows we're seeing, are mixing this deterministic and AI or LLM calls along with agentic orchestration processes altogether. And so this is your purest form of, you know, hybrid orchestration as we talk about it. So how do the patterns wind up coming together? And I'm gonna talk about the first one. I'm gonna pass over Peter to talk about the second one. The first one is exactly what I just showed. Right? You have this workflow as a step pattern. You might schedule something. This could be time-based. It could be event-based based off of another system. Ultimately, you'll have maybe an LLM task. But when this agentic system happens, it's gonna spin off and run the agents. There could be three. There could be 20. We have one customer that has, you know, 15 different agents running in AWS that is doing a bunch of root cause analysis, and they're working that into a workflow with us that will automate workflow analysis across the board. This is that sort of step, and I have a workflow that I'll share with you guys at the end of this that explains that one. Peter, the agentic task kicking things off is really interesting and probably a future state for a lot of what's going on. Can you talk through this a little bit? Yeah. This is, you know, when we talk about having a lot of deterministic automation out there presently, you know, what what we see also is that one way to kind of help make agents, as Dan said, just, you know, kinda make sure that they that they do the thing right in a in a predictable way is for them to kick off a workflow that either exists or that you develop, and they can kind of sit there and determine which is the best workflow to run. A lot of these workflows, you know, have variables that affect the behavior of how those workflows work, you can set the variables and make them behave differently on the... When they actually run. And so the agent can be really good about picking out the workflow, setting any parameters that make it work a certain way, and then you get this very good deterministic outcome from automation. But you also have that intelligence upfront that can actually dynamically change it based on some of the information coming in. So this use case is one that we really see as another way to do this this hybrid world, but something that gives organizations the ability to do things repeatedly, but also build intelligence up front. Yeah. And we're referring to this as a governed workflow. So we... If I look at this next slide, it's exactly the same thing that we just look like... Looked at in the pattern that Peter was explained, but just said a little bit different. In this case, an agent is being used. It's been given a task. Right? And that task may be to go, let's say, spin up a data pipeline someplace and retrieve the data to continue the rest of his task. When it needs to trigger that data pipeline, it would make a tool call to UAC. Right? And at that point, the governed workflow, which is no different than the workflow that Peter just shared here, kicks off in UAC. This governed workflow serves as a buffer between the agent and the actual enterprise systems, thus being governed. It keeps everything in check. It makes it so that the agent never actually touches the mission-critical processes and systems that you have in place. This buffer is incredibly important. It's where all your policy approvals, credentials, dependencies, validation, retries, and audit live. Right? And it... The agent cannot be used in a malicious or bad way because this governed workflow will protect the actual enterprise systems. Governed workflows is gonna be something you hear us talking a lot about through this series and going forward. We think it is absolutely the way of the future. Alright. So what we have here is a blank architecture. This blank architecture is what we're calling the enterprise hybrid AI architecture. What I'm gonna do is I'm gonna walk through every step of this. This is the mental model, the way that you can think about hooking all of this up and making this hybrid world a reality for you. So it's blank here. We're gonna fill it up. So let's start with sources and triggers. Sources and triggers don't change much from what you're using today. Today, you're using APIs. You're using events that may be a system event. So you're also using time-based triggers. So at 02:00 in the morning, go ahead and run this workflow. And also might have a human that triggers a workflow. Right? The real ad in the agentic world is that you're gonna have agents being able to trigger workflows as well, governed workflows like we just talked about. And so you have agents that are initiating work, and then you'll also have agents that are performing work, and we will show that. So any one of these four boxes can trigger a workflow now. The hybrid orchestration control plane is really where UAC comes in. This is where you can create the workflows, those governed workflows, the deterministic workflows, or even the hybrid workflows. This is where your workflow orchestration runtime lives. This goes back to state. Right? Dan talked... It's talked about state being important. I personally think nobody talks about the state, and everybody should be talking about state. This is sort of the invisible layer that has the potential to make AI work or not. Right? So there is some state within the agents. There's state within UAC, and it's taking dots between those things that give the context. The business process context across the whole business is given to the agent through UAC. So it knows if there is still workflow down or if there's a service, you know, area that needs to be set up or there's the... A tool is down someplace because it's under service, and that's why the workflow is not working. Like, it provides that intelligence back to the agent. Next, you have guardrails and compliance. A lot of this is what people are billing in agents today. They're trying to use prompts to do it. Prompts are just instructions. They're not really guardrails. They're not really going to stop the agent from doing or finding ways to execute things because you can't think of every possible scenario and put it in a guardrail. And the longer the guardrails or the prompts get- the system prompts- the more error-prone they are. So you can add guardrails. For sure, they're important, those prompts, those system prompts that people usually use. But the business rules, the policies, and the governance controls that UAC has is what really ties this and makes it safe and reliable. Next, of course, you'll have human loop ability through UAC like you do today. Everybody on the call has probably seen Universal Portal as a way that you can do that, but you can also do it through UAC, the controller itself, or any of the integrations we have with Teams or ServiceNow or Slack. The ability to be alerted when something needs to be checked to make sure that it's not wrong before it moves on to the next step is incredibly important in AI era. And, of course, you get all your SLAs and recording that is completely necessary when you're talking about audit or dashboards or getting that telemetry data out from an observability platform. It's really what helps you manage the entire business. Now the hybrid orchestration control plan is also where that deterministic automation, the I... AI-enhanced automation, the agentic delegation, I. E. Calling out the agentic systems is controlled and managed as well. So this is sort of the... You know, I always hate taking our product and making it the center big box, but it's really true in this case. Right? There is a lot that happens here, but UAC alone doesn't do everything that's required within a hybrid AI architecture. UAC is this hybrid orchestration control plan that helps connect the dots between the sources, the triggers, and the next three stages. So in this a... An agent governance stage, this is where you'll be seeing AI model gateways or AI gateways. The one that we're running into most right now is Kong, but there's lots of different ones out there. The API vendors, the Boomies of the world, the MuleSoft, they're trying to get in this market. It's just Kong is easy and reliable right now. In a current state, this is where people who are using agents or making agents available to their employees, or even models available to their employees, everything passes through the model or AI gateway because they can control cost that way. They can cap how many times an agent runs or how many times the model is used, or it can help select which model to use. Maybe the process you're running needs to have a less expensive model than, you know, using the best of the best or high or strong or powerful models to do something simple. So your gateway is how people are controlling that right now. There are gonna be capability and endpoint registries that are introduced. You're gonna have a catalog of agents that can be introduced. Some of that can be managed inside of UAC, but you're gonna have agents all over the place. There are a lot of companies that are positioning themselves to be sort of a control tower. Not a control plane, but a control tower where they are gonna capture all of the agents that are available throughout your entire enterprise ecosystem. This is where you'll go to find out which MCP servers are available, APIs, and different workflows across the business. Again, in some cases, we'll take over some of this, but in other cases, we'll just tap into those tools like we do today. It could be just as simple as, you know, a security key or something that we tap into like you would through UAC if you're tapping into any other system. Next, we have FinOps. Now, FinOps, I just looked at the, today, actually this morning, received it, the hype cycle for AI from Gartner. And these FinOps tools are at the peak of hype right now. They're all the way at the tippity-top of that hype cycle chart, if you guys are familiar with it. And it's because they're talking about a lot. There just hasn't been a tool that everybody's surrounded themselves on. Like, there hasn't been a tool that has emerged as the one master FinOps tool, but it will. And many of you will wind up wanting to connect into these FinOps applications, and we do that through UAC. You would pull in that data and share it back to the FinOps tool as a... As an integration. And then, of course, you'll have evaluation and quality tooling. And there is... This tool tool exists today. There's a lot of it out there, but it's it's sort of scattered in. Some of it's inside of other agentic platforms. Like, you may have an agent that evaluates the quality of another agent. You may have tooling that does that. So this is coming as well, but this is really that whole layer that we would connect into to help manage the agentic pieces of the business. Next layer here is your interoperability and execution layer, and this is really important. So just like today, you're gonna have REST APIs. You're gonna have webhooks. You're gonna have events that trigger automation. But in addition to that, which didn't exist three years ago or or even two years ago or the model context protocols, UAC in v8.1, just released the latest update of our model context protocol (MCP), which allows you to actually connect to UAC from Claude and trigger automation inside of UAC from Claude or any other, you know, your choice of model harnesses that exist out there, ChatGPT, those sorts of things. But MCP is also how we may integrate, in some cases, with other agents or other systems. There's even a future state where MCP or agent-2-agent protocols, A2A, is another emerging piece that actually is how agents would communicate with each other and pass information back and forth. So you may have an agent that lives in, say, ServiceNow or SAP that wants to talk to the agent in UAC to perform a task and to do some back-and-forth agentic work. So there's... We're not seeing a lot of that yet, but that's sort of the vision for where people are going. So, clearly, the agentic piece is where... Or the agent-to-agent is what's emerging as the sort of go-to there. But, you know, the world's changing fast. Today, it's agent-to agent could be something different later, and we'll adapt that as well. The other place you're gonna see agents, and this is where you're actually connecting the agents. Again, you probably... You can connect directly to agents, but you probably, in most future states, will go through an AI model gateway and, like, Kong. And, they're gonna have native executors. Now we have a nice Stonebranch logo here because we'll, next year, begin having, you'll begin having the ability to build agents just like you do in any other Agentic platform within UAC. But this could be any native agent. So you're gonna have native agents in applications all over this place. Basically, any application... Enterprise application you have is busy adding agents to it. I use HubSpot and Salesforce a lot. Both have agents inside of them. SAP, ServiceNow, they are busy bees building up agents all over the place, and this is your native agent that you would go and connect to as part of a process. The other place where you'll have agents is this ex... External agent runtime. This is your inner loop. This is your agentic orchestration places, and where you're actually building multiple agents to do processes. So UAC, again, would come in and integrate with each one of these things or integrate through an AI platform to go ahead and and trigger agents or interact with agents or interact with models. So this is a very important layer that helps you connect. And then, of course, this is the same... In this final layer, the same enterprise landscape that you connect to today, whether it's mainframe, on prem, cloud systems. I won't read them all, but this is what you're automating today. UAC still automates these. The agents may wanna touch these, but our strong opinion, and I'd say the opinion of the growing masses at the moment, is do not let agents touch your enterprise systems. It will go haywire at some point. So you use a governed workflow. You use an orchestration platform like UAC to help create that governed workflow and the buffer between the tools. You never have to expose these enterprise systems directly to agents. So that is the enterprise architecture. The, last thing that goes basically vertically all the way down all five of the layers are additional controls. These controls will live in GRC tools. They'll live in your identity and security tools. You'll have audit and compliance that you can pull from both UAC as well as observability tools that we're connected to. You'll have policy and guardrail services. Some of that will come out of UAC. Some of them may come from third-party tools as well, and then, obviously, the resilience and reliability that retries timeouts, and controlling the blast radius of anything that goes wrong with this. And this will largely be inside of UAC. So the full picture looks like this. You have your sources and triggers. You have your hybrid orchestration control plane, which, again, is where Universal Automation and Stonebranch live. You have your AI agent governance. You have your interoperability and execution layers to connect to all of the AI tooling out there, and then you have your enterprise landscapes that are what you're actually automating. Right? And then, of course, you have the layers across everything to help connect across all of these layers from a centralized control plan, which would be UAC.
