Agentic AI isn’t coming — it’s here and already changing everything.Irina Gutman, Global Leader of AI Professional Services at Salesforce, breaks down what agentic AI really is and why it’s a huge leap beyond predictive and generative AI. We get into why your first AI agent should be boring (and repeatable), and why building the tech is easy compared to rewiring your people, processes, and leadership models.Irina shares why businesses need strong guardrails, real operating models, and why AI adoption without organizational readiness is a recipe for disaster. We also talk about the skills humans actually need to stay relevant, how to spot hidden risks, and why the future belongs to companies who rethink their structure — not just their tools. Key Moments: 00:00: Irina Gutman Explains Salesforce’s AI Agents03:03: Predictive, Generative, and Agentic AI — What's the Difference?05:20: How Agentic AI Thinks and Acts08:32: Chatbots vs. AI Agents: Why It Matters14:22: The 5 Building Blocks of an AI Agent18:13: Organizational Readiness: New Skills, New Roles22:26: The Right Way to Start with AI Agents26:27: Future-Proof Your AI Strategy29:53: Rethinking the Operating Model for AI32:45: Upskilling is Non-Negotiable35:14: Vendors Can Help You Be AI-Ready36:25: Rethinking Change Management for Agentic AI42:38: What’s Next: Multi-Agent Collaboration48:09: Building AI Responsibly: Guardrails and Risk51:39: Real-World AI: A Standout Customer Experience –Are your teams facing growing demands? Join CX leaders transforming their AI strategy with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org
Chapter 1: What is agentic AI and why is it important?
Give me an example of the sexiest, the most complex, advanced agent that you've ever built. And my answer is, sure, we can do that. But actually, you want the most boring, the most repeatable, the most low-hanging fruit agent to start with.
I know people who are hiring AI managers, people whose role is just simply to manage the agents. The thing about agentic AI that is just so incredible, it can think for itself.
Nobody wants to be a headline for the wrong reason or have a lawsuit. Designing experience between an agent and a human is another flavor of a skill that is very new. Critical component of that operating model is that business and IT partnership. Turning on tech is the easiest part. It requires constant monitoring, updating, and kind of having a pulse on where it is and where it needs to go.
Chapter 2: How does agentic AI differ from predictive and generative AI?
When I talk to my innovation team, I'm constantly blown away how they take capability of Salesforce AI product and turn it into the solution that elevates customer experience to a new level. I'm like, what? We can do that? This is not science fiction.
Hello, everyone. Welcome to Experts of Experience. I'm your host, Lauren Wood. Today, we are diving into a very, very big topic, agentic AI. and what it means for businesses, for workforces, for leaders, and really humans at large.
And we are joined by Irina Gutmann, the global leader of AI professional services at Salesforce, who is here for the second time because so much has happened since we talked to her six months ago. And we're really going to dive into what is happening in AI when it comes to the agentic AI layer? How are businesses implementing it? How are organizations adapting?
And really, how can we powerfully leverage this technology safely? So we're going to be talking about all of that today. I am so excited for this episode. I've literally been like giddy for it. Irina, thank you so much for coming back. So great to have you.
Thank you, Lauren. Super excited to be here and share with you what's happening in AI world. And as you mentioned, a lot had happened. So in preparation for this episode, I was actually reflecting and thinking, we didn't talk like that long ago.
And when we first started and talked through different types of AI and evolution of AI, I was mentioning that agentic technology is somewhere there on the horizon, but let's just not talk about it yet. Literally a few months later, not only we're talking about it, it is the primary focus for Salesforce, as well as many tech companies.
So very happy to be with you and unpack this very relevant topic.
Amazing. I want to make sure that everyone is caught up to what agentic AI is, because it is different from the generative AI and the predictive AI. So can you just really quickly define those different types of AI and where we are today?
Absolutely. Yes. Let's get down to the definition and kind of bring everyone up to speed. Let's start with predictive AI, which probably been around for longer than all the other AIs. And the definition is literally in the name for all those technology categories. Predictive AI focuses on making predictions based on data, based on rule, and based on the structure that we provided with.
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Chapter 3: What are the key components of an AI agent?
It still uses data, but it uses the data to almost reason on what action to take and how to interact. And yes, it interacts to us based on the instruction we're provided with, but it uses natural language processing and converses with us in the natural language.
So to summarize, we still have rules, we still have data, we still have actions and instructions, but this agent uses this information to reason, to make decision, and to converse with us in natural language.
One of the examples of an agent, and we will talk further about different type of agent, but the funnest example that I can give is if you've been to Bay Area, they have VAMOS, which is the self-driverless car. This is an example of autonomous technology that operates without a human, makes decision of how to switch lanes.
It takes the information provided to it, instruction provided to it, as well as the data that it takes from the road and and makes decision how to switch lanes, how to accelerate, when to stop, etc.
I love that you use Waymo as an example, because if you are in a city that has Waymo and you see them driving around, it's this crazy experience. I mean, I'm still like, I can't believe we are here, but we are. They are driverless cars. They are following the rules of the road. And when you think about it, there are pretty strict guidelines for how to drive. But then there is also this element of
intuition that is required of, oh, I feel like that car is coming at me really fast. Maybe I should slow down and let it pass or anything like that. And the thing about agentic AI that is just so incredible, which we're going to dive into so much more, but the thing about it that is so incredible is that it can think for itself. All right. Kind of creepy, but here we are.
And there's so much there's so much opportunity.
We refer to it as a reasoning engine, which kind of sort of an evolution of thing. One step further, it also has memory. So it can tap in into the previous data and information and make decision not only on the data provided at this time, but also from the learnings of data that it has available to it before. So we refer to it as it has memory in addition to reasoning.
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Chapter 4: Why is organizational readiness critical for AI adoption?
Great. We love this. I want to make sure that we ultra define this for all the folks in customer experience, because I do hear chatbots and AI agents being intermingled. And can you just now apply this definition to the difference between a chatbot and an AI agent?
Perfect. So let's keep in mind what we just learned about the genetic AI, right? Components of reasoning. Memory, conversation, being able to infer action based on the information that's available to it and compare it to the chatbot. Chatbot follows a very prescribed process flow. It is pre-programmed how to intake information and how to respond based on that prescribed information.
It cannot deviate. It cannot change a course of action or interaction based on that process flow. And it does not understand natural language. For example, let's say we're using a chatbot, and don't take me wrong, chatbots are super effective and super fast for addressing repeatable processes with very few deviations.
Chapter 5: How should organizations begin implementing AI agents?
And in fact, if you have that instance that it's literally a repeatable process that has prescribed process flow with very few deviations, maybe chatbot is an effective technology. However, if we, let's say, ask a chatbot, where is my order? You have to ask it in a certain way and provide an order number. Otherwise, poor chatbot might be lost.
If you interact with an agent, you say, hey, the thing that I wanted from you last week, what's up with that? An agent literally should be able to interpret the slang, the various variation of the language, and come back to you saying, oh, you're looking to find out the status about your order. Let me get that for you. Would you mind providing an order number?
Agents should be able to infer, based on the information provided to it, what the next step should be and what response should be to a human. Agent could pick up on the tone and change the way if I speak in slang, maybe in an interaction or two, agent will start responding to me in some slang versus a formal language. Chat cannot deviate from that.
Chapter 6: What are the skills required for working with AI agents?
Every company using agent, they might want that agent to reflect the tone of this company to begin with. More formal, more casual, cool versus some other type of a flow representing company's brand. You have that flexibility with an agent. Chatbot would not be able to do that.
I think from a consumer standpoint, it is, I'm just so excited as a consumer because we've all had that experience where we're on with a chat bot and it is not understanding what we are trying to say. We are, you know, maybe there was a spelling mistake and it's just like taking us a totally different direction and it's just downright frustrating. But with an agent, it's,
I feel so much more willing and able to actually solve my issue with that agent. Like before I felt, oh, chatbot, you're just deflecting me because you don't want to talk to me. You don't want to hear what my problem is. And now with an agent, I feel seen and heard and my problems getting solved faster. So it's really like this incredible balance between
an actual human who can be slower at answering these things at times. And, you know, the chatbot that just wasn't solving my problem at all. So yeah, I'm really excited about this. So last time you were on, we talked a lot about human in the loop. But this is now advancing. How are you working with agents now? If you can kind of give us an overview of that.
Absolutely. And by the way, yes, you are allowed to make spelling mistakes with agents, which is awesome.
Great, because I make them all the time.
But going back to your question, we did refer to a previous iteration of generative AI as a human in the loop, meaning that human makes the checks and final decisions. Now you have a technology that almost operates agentically. And we change our phraseology from human in the loop to human plus AI.
We now have AI augmenting and expanding humans' capability with an assistant of this digital, let's say, assistant called agent. Humans still have a decision power, and we provide agents with instructions what to do or not to do.
But once those instructions are provided, that agent is able to assist us in a way that literally translates to human plus AI versus human is in the loop or in the hell making decisions for AI. Mm-hmm.
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Chapter 7: How can businesses ensure responsible AI use?
Chapter 8: What does the future of AI agents look like?
Because it's... really being like trained through those types of interactions of feedback where it's operating on its own. We don't have to say, yes, go do that. It's doing it, but we now need to work with it in helping it to grow.
Absolutely. And before we talk about agent as potentially when people refer to it as digital labor, I think it would be helpful to understand main components of an agent. Because that will help us have conversation about agent being that digital employee. Agents have five components. We're going to start with role. Just like a human will have a job description,
an agent is going to play a specific role. For example, let's say this is going to be a customer service agent. And the job of this customer service agent is to answer a specific set of customer questions. So we just define the role of an agent. Next step is action. What is it actually going to do?
Well, we want it to answer FAQ, frequently asked questions, and we want it to do certain things based on the instructions that we're going to do, that we're going to give it. And we also don't want it to do certain things. So actions are based on instructions that provide the agent. And it always includes things that we wanted to do and absolutely things that we don't want it to do.
Which brings me to our next component. It is called guardrails. Guardrails are super, super critical. When you're putting autonomous technology in front of a customer, and by the way, we'll talk about it, agent being assistive internal to the company versus customer facing agent.
Mm-hmm.
But let's say in our example, we're talking about a customer facing agent that is playing a role of the customer service representative. We define its role. We know it needs to take specific action, but now we're going to give it guardrails to tell it what it's not allowed to do. For example, it can only answer questions about the order and whether shipping is free or
or you have to pay for based on some zip code. But if it goes beyond a certain set of questions that agent knows how to answer, it has to hand off to a human. That's where human plus AI comes in, is that always, always, always, as part of those instructions or guardrails, there is an instruction on when to hand off to a human. The next component that we need to discuss is data.
It can't take any of the actions or follow any instruction unless it has information, the knowledge to make those decisions and take those steps on. So data is the fourth component. And the last component is called channel. How is it interacting with us? Is it living on the customer's website? Is it an employee agent that lives in Slack?
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