This conversation takes place at Dreamforce 2026 and focuses on artificial intelligence agents, integrated service platforms and outcome-based service. The discussion examines service automation, outcome-based pricing and strategies to drive measurable customer outcomes.
Kishan Chetan of Salesforce is executive vice president and general manager for Agentforce and Service Cloud. Chetan describes the evolution of AI agents from question-and-answer tools to outcome-driven automation, emphasizing integration of context knowledge and workflows. They highlight role-specific agents, operational observability and seamless human handoffs as critical elements for measurable business impact.
Alison Kosik of theCUBE Research and George Gilbert of theCUBE Research guide the discussion and explore real deployments, including airline examples and enterprise implementations. The conversation covers practical implementation steps, analytics-driven tuning, shortened deployment cycles and tighter IT and business collaboration.
According to Chetan, customers see clear return on investment with higher satisfaction and productivity enabled by Agentforce’s integrated stack and outcome-based pricing that charges per resolution. Chetan emphasizes agent optimization loops and continuous observability. Gilbert notes Salesforce’s ability to assemble context knowledge and workflows reduces time to value and supports more efficient customer service operations.
Watch the full conversation for practical guidance on deploying AI agents, integrating service platforms and implementing outcome-based service models.
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Kishan Chetan, Salesforce
This conversation takes place at Dreamforce 2026 and focuses on artificial intelligence agents, integrated service platforms and outcome-based service. The discussion examines service automation, outcome-based pricing and strategies to drive measurable customer outcomes.
Kishan Chetan of Salesforce is executive vice president and general manager for Agentforce and Service Cloud. Chetan describes the evolution of AI agents from question-and-answer tools to outcome-driven automation, emphasizing integration of context knowledge and workflows. They highlight role-specific agents, operational observability and seamless human handoffs as critical elements for measurable business impact.
Alison Kosik of theCUBE Research and George Gilbert of theCUBE Research guide the discussion and explore real deployments, including airline examples and enterprise implementations. The conversation covers practical implementation steps, analytics-driven tuning, shortened deployment cycles and tighter IT and business collaboration.
According to Chetan, customers see clear return on investment with higher satisfaction and productivity enabled by Agentforce’s integrated stack and outcome-based pricing that charges per resolution. Chetan emphasizes agent optimization loops and continuous observability. Gilbert notes Salesforce’s ability to assemble context knowledge and workflows reduces time to value and supports more efficient customer service operations.
Watch the full conversation for practical guidance on deploying AI agents, integrating service platforms and implementing outcome-based service models.
EVP and GM, Agentforce and Service CloudSalesforce
In this interview from Dreamforce 2026, Kishan Chetan, executive vice president and general manager of Agentforce and Service Cloud at Salesforce, joins theCUBE's Alison Kosik and George Gilbert to discuss how the agentic enterprise is moving from experimentation to measurable business outcomes. Chetan details how Salesforce ties together context, knowledge and workflows so agents can act rather than merely respond, citing Southwest Airlines' use of the Casey agent to handle brand-specific passenger questions while boosting rep productivity. He explains the s...Read more
exploreKeep Exploring
What significant changes have occurred in the past year in what AI agents can do for customers?add
What are the essential elements an automated agent needs to effectively assist users, be personalized and contextual, and take actions on their behalf?add
What is Southwest achieving with the Help Agent (Casey), and how are those outcomes being measured?add
How do you continuously tune and improve conversational agents in production, what challenges do organizations face in doing so, and how must Salesforce and customers engage differently to implement them?add
>> Welcome to theCUBE NYSE Wired program and community coverage of Dreamforce 2026. We're coming to you live from San Francisco. I'm Alison Kosik alongside George Gilbert, and I'm excited to get into this next conversation, because we're seeing AI agents move beyond answering questions and actually take actions. And it's going to be interesting to talk about what that means for businesses and customers.
George Gilbert
>> Yeah, and what Salesforce has done probably more than any other vendor was put all the pieces together so that the customers don't have to, so it just works. And we get beyond the request response and we can get to outcomes. And for that, you need the data, the applications, the agents, and it all becomes essentially a harness that does stuff.
Alison Kosik
>> Yeah, absolutely. All right, let's bring in our next guest. It's Kishan Chetan. He's the Executive Vice President and General Manager of Agentforce and Service Cloud with Salesforce. Welcome to theCUBE. So great to have you.
Kishan Chetan
>> Thank you so much for having me.
Alison Kosik
>> Nice to see you as well. We're here at Dreamforce outside. It's a beautiful day here in San Francisco. at Dreamforce, agents are clearly taking center stage.
George Gilbert
>> Yeah.
Alison Kosik
>> I'm curious about this because you lead Agentforce and Service Cloud. What for you has changed dramatically in the past year about what AI agents can do for customers?
Kishan Chetan
>> Yeah, look, first of all, thank you for having me. It's fantastic to be here. A lot of energy, a lot of excitement here. Agents are driving a lot of it. But I think a big part of what I've seen over the last year, which has really changed, is how much customers have adopted this technology. We had it in our customer— in our keynote for Agentforce, and it was all about ROI. We had customers stand up on stage and speak about how they're getting great ROI. So in the case of service, the ROI could be higher customer satisfaction, it could be the productivity because of lower costs, but lower cost driving more implies you can do more service. So we're seeing very clear ROI metrics. And Marc actually spoke about this in his keynote, Marc Benioff. He spoke about how Casey, our agent, has helped handle 5 million conversations over the last two or three years and how we created a bunch of pipeline and managed pipeline with our sales agents. And that's the type of value we are adding. It's about the ROI.
George Gilbert
>> So, Kishan, putting the pieces together, for the first couple years of agents, we had a lot of technologies. The technology side of the house was often empowered to get something stood up really quickly and it was often a standalone agent development toolkit. And there are more of those than you can shake a stick at.
Kishan Chetan
>> Yeah.
George Gilbert
>> Like, help, help us understand why it's so difficult to tie in the context from all the systems of your existing systems, but also then the actions that are in the normal agent. A tool is how it accomplishes something. But in Salesforce, the tool is how you get to an outcome. Help us understand how by putting— supplying all the pieces, it makes it so much easier for our customer to get to those outcomes.
Kishan Chetan
>> Yeah, fantastic. Look, what you want the agents to do is at the end of the day, it answers a set of questions and it takes actions on your behalf. I'm oversimplifying this. Of course, there's a lot more to that, right? And then of course, you want it to be personalized. You want it to be contextual because if I'm a patient who go— if I'm going and speaking to my pharmacy, I want that agent to know who I am and ideally know what my prescriptions are, to give an example. So that all of those three need to come together. So it needs to know more about me. And that's where all of the technologies we have to bring the right context of who you are, and what you've done in the past are important. The second is what we call knowledge. The agent is only as good as what it knows, what it's trained on, and being able to take all of this corpus of knowledge that you have within your organization your SOPs, your documents, your historical calls, getting that and using that to build that agent, that becomes important. And finally, the last thing is it actually needs to do something about it. If I'm calling a bank to change my address or create a new account, it actually needs to go do that. And that's where the workflows matter, and that's in Salesforce. So the combination of these three, knowing who you are, being able to have the agent trained, and then being able to do something about it, that becomes really important.
George Gilbert
>> So I would gather that Southwest is actually having a fair amount of success with the Help Agent, Casey.
Kishan Chetan
>> That's right.
George Gilbert
>> Okay. So tell us what they're accomplishing and how they measure the outcome if they do.
Kishan Chetan
>> Yeah. Yeah. Great question. And look, Southwest was actually on our Service Cloud keynote last year in Dreamforce. And it was fantastic because what they wanted to do is they wanted to give a very specific contextual answer to their passengers who were there. So they wanted to ask about flight questions or questions about what's available. They wanted to get the right brand relevant answers and a reasonable percentage of them essentially are now answered by the agent. Then they also wanted to have the AI and agents necessary for making their own reps very productive. So which basically means that when a service call came in and a human needed to address that, that needed to be addressed properly with the right brand tone. And both of those are things that Southwest was achieving. It was a great keynote. We actually had them also yesterday on the Agentforce keynote. So that's a continuing success over the last year and a half. Oi bah!
George Gilbert
>> So how do they, do they measure a cost benefit? Because, we've all been struggling for, like trying to understand how do you price, how do you— how does the vendor capture value? How does the customer measure the value that's been delivered? Help— I know there are different variables. There are different ways of doing that now that Salesforce has been evolving.
Kishan Chetan
>> Absolutely. Look, we've been evolving our pricing model over time. But just to be very concrete here, one of the key things in service, the good news about service is you have a good sense of whether a customer's question or their request or their issue has been resolved. So we've actually moved to an outcome-based pricing, which basically means our customers pay us only when their customers' issues are resolved. So which means that if somebody has come in and asked a question about needing to reschedule my flight and that reschedule actually happened, that's a resolution. If that reschedule needed to go to a human because they didn't solve it, that's not a resolution. So that's how you essentially have a very clear way of measuring what the technology is doing.
Alison Kosik
>> Yeah, what should be the ideal handoff between an AI agent and a human? When it comes to customer service?
Kishan Chetan
>> That ideal handoff really has to be a few things. One is there are several cases where the handoff is actually necessary. If I'm in the luxury industry and I want to ask questions about a new handbag that I want to buy, I want to speak to somebody live about it. So that handoff, the ideal handoff, needs to have the right context. It needs to know everything that's happened so far, what's happened in the conversation, what was the customer sentiment, What is the customer looking for? And pass all of that so that the human doesn't have to wade through all of that. And then the human needs to also have— the rep needs to have a good summary and something from the tool telling you that, hey, she's looking for this handbag. Maybe these three are the right ones. And while you're doing that, maybe you can also have this other service that she can have, which is around maintenance of the bag. Just to give an example, that is what ideal experience is like.
George Gilbert
>> So let's dive in a little more this year with the role-specific agents like Casey.
Kishan Chetan
>> Yeah.
George Gilbert
>> Like what went into, you know, what are the ingredients that saved the customer from having to roll their own? What's in Casey that last year they would have had to build themselves?
Kishan Chetan
>> Yeah, a few key things. The first one is ease. We've done a lot of things to make it easier to set up, which is, you can automatically connect to what I said about knowledge and connect to the context of what the agent is supposed to do. Second, it's easy to connect it to the channels that the agent is working on. Is it on the phone? Is it a chat? Is it on WhatsApp? That part becomes easy. But what the real secret sauce is, is this whole idea of an agent to kind of build and optimize the agent. And what it does is it looks at my historical conversations Let's say somebody has come in and had a bunch of phone calls about needing to change my address or updating something. You look at all that historical corpus of information, your phone calls, you take that and make that agent with that so that the agent is automatically starting with that context. So that's a big part of it. Then second, you have this out-of-the-box analytics, which means that, you know, in a service context, you'd care about resolution rates, you care about handle time. You care about how many times it's escalated. These are metrics which are very specific to that context. So it's building the agent, it's optimizing the agent, it's measuring the effectiveness of the agent. And finally, we make that engagement between the agent and the human seamless and making that all seamless. So those are the 4 things that we've done out of the box.
George Gilbert
>> Okay. So now, how much time might that save a customer, you know, from rolling their own? Like if they just get a generic agent development kit and let's say they have Salesforce, but then they would have to pull data from multiple sources. They would have to attach, you know, to Salesforce objects or the APIs. Like how much time does that save?
Kishan Chetan
>> And we've done a bunch of work broadly. We also have what we call Enterprise Knowledge, which is We make it easy with our Data 360 platform to get data from multiple places. So we've solved some of those problems to make it easier. To answer your specific question, we have customers who have been able to set up and get up and running in a matter of weeks. Right. And if you build everything from scratch, depending on where you are, it could take several weeks to months. Again, I think the saving is pretty significant in terms of setup. But what I wanted to point out too is the setup is important. But what's really important is once the agent is up and running, can we make sure that the agent is actually doing the right thing, that it continues to do the right thing as your business processes change, you introduce new products. That's where the real power is in the optimization, not just setting it up.
George Gilbert
>> Oh, so there's a loop. That loop. So that's why you emphasize the analytics. Yes, because you're going to always be tuning it. And so what's— what— oh, this is already a learning for us, which is it's not just getting it up and running, it's that it's tied into the outcomes that you can measure in Salesforce.
Kishan Chetan
>> Exactly.
George Gilbert
>> Tells you how to continually tune the agent. And what would that tuning look like?
Kishan Chetan
>> as an example, if you have an agent which is in a retail industry and you realize that whenever there's an order where you're adding new products, it always needs to go to a human. And that's what you're seeing from your analytics, you perhaps need to go and understand why is that the case? Do I have something which is missing in my corpus of information? Do I need to actually create a workflow so that it can automate that? That is what the tuning is doing. It's identifying gaps in your agents' build, in your knowledge, in your workflows that you can then further automate.
Alison Kosik
>> And what are the challenges in this that you're finding?
Kishan Chetan
>> The challenges that customers are finding quite often is a few-fold. One is acquiring the knowledge and all of the data that's there. The processes that are important are not always documented somewhere. It's in somebody's head. It might be in some historical phone calls. So taking that information and making it available for the agent to use, that's a pretty big deal. That's the reason why our Agent Optimizer looks at historical conversations and automatically learns from it. And the second one to your question on the challenge, is to make sure that you can orchestrate the work in different systems. So for example, if I'm running an airline or a train service, one of the things you want the agent to do is to book a ticket or reschedule a ticket or maybe book a lunch that needs integration to other systems. Being able to build that integration is going to take the time.
George Gilbert
>> So also, how does Salesforce and the customer have to engage differently now, where you have to involve the technology side of the house. There's a business owner for the outcome, but then Salesforce also maybe has to have more professional services upfront or bring in a service partner in a way that they didn't before, where the customer has to The technology side of the house is the enabler. The business side of the house, owns the outcome. And those two sides have to work together in a way they didn't have to before. And then Salesforce has to maybe orchestrate that. Is that—
Kishan Chetan
>> Yeah, there's— a lot of that used to happen before, but there are a couple of things I think which have changed. One is the cycle of how quickly this has to happen has been really shortened. So you want to get the benefits in weeks, not months, days, not weeks, right? That's one thing. The second thing is in the previous world, you do a— you demo your capability. People would look at that capability, then they'll get a partner. You'll try to understand your processes, document it and have an implementation. You don't— you can't do that in the Agentic world because what you really want is you don't want a demo. You want something working so that you can understand how it's working and continue to tune it. And in order to do that, we bring our own, what we call FDE, a forward deployed engineer. Sometimes it's a partner as well. And our product teams are quite often engaged with both the business leader and the IT leader. So shorter, very focused, start small, show success, and then grow.
George Gilbert
>> And that— okay, and then that loop, does that include when you're learning? Because this is really important. There's this— does that loop include, just to get into the technical terms, the observability traces. Is that what you're
Kishan Chetan
>> learning? I spoke about analytics and I spoke about optimization. You can't analyze and optimize unless you have great observability. And observability is going to be different across different channels. What you need to do for voice observability is very different from text observability. So observability and understanding how well it's doing, have you tested it before, can you continue to optimize it, becomes crucial.
George Gilbert
>> But that's part of what you're learning from. That's it. That's right. Okay, okay. So just to sum up really quickly, that's why you needed this integrated approach, because you can get started really quickly with a standalone tool, but you're not going to be able to run that sort of learning loop and that optimization unless you have all the pieces.
Kishan Chetan
>> Precisely. And there's one part of it, just to reiterate that, and it might be obvious, in our mind, but just to reiterate it, that optimization that you need to have happen does not need to happen only over agents. It needs to happen over agents and humans. So the real thing is, let's say I'm trying to— somebody calls in working with an agent, it went to a human and the human did something. You really want to optimize the agent based on what the human also did. And that's the power. That's why the integrated platform really matters. And you also want to do it not just for service, you want to do it based on what happened in a service transaction and a commerce transaction and a marketing transaction, bringing it together is what really, really drives success.
Alison Kosik
>> Okay, take out your crystal ball for the final answer. Where are you going over the next year? How do you see things dramatically changing?
Kishan Chetan
>> I think, for me, the one which I'm really focused on is helping customers drive success and really drive metrics which are great business metrics that lead to success. And the big technology thing that I expect to happen. We've got this optimization that we've launched. I want this automation to be much bigger and much broader. And you also heard about Agentforce, where a lot of our capabilities are available where the customers are. And that's really important. We spoke a lot about customer service, but my team also owns our employee service, IT service, HR, and we want that service to be available in Slack, available in Cloud, available everywhere where people are. So optimizing the agents and being available everywhere for all of the functions. That's, I think, what we are looking to do over the next 12 months.
Alison Kosik
>> All right, Kishan, great conversation. Thanks for stopping by theCUBE.
Kishan Chetan
>> Oh, thanks for having me. Very nice to meet you.
Alison Kosik
>> Nice to meet you as well. And you're watching theCUBE, the leader in live tech coverage and in-depth expert analysis. We'll be right back.
Alison Kosik
George Gilbert