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Agentic AI can autonomously and proactively take actions to reach goals, such as analyzing complex databases and sending personalized communications to customers.
AI agents can help with every step of the customer journey, helping marketers create personalization at scale.
While agents can help lighten workloads and expedite processes, human marketers must stay in the loop to ensure the quality of content and messaging.
OpenAI's ChatGPT sparked an AI revolution and, just about two years later, another form of artificial intelligence is changing the way businesses interact with customers: Agentic AI. Agentic AI takes the features of generative AI and chatbots a step further by completing actions independently. These AI agents can perform concrete tasks, like messaging a prospect or searching for leads, all on their own.
When it comes to optimizing and maximizing the customer journey, AI agents have the potential to impact every step, from awareness to advocacy. We'll examine how you can use these advancements to improve the customer experience, as well as discuss potential challenges, benefits, and best practices for using Agentic AI.
Agentic AI is a type of AI that can proactively and autonomously take action to achieve goals, without the need for constant human guidance. An AI agent does not need to be explicitly programmed with code to carry out the specific tasks it needs to do. Instead, it learns from examples on what to do.
At a deeper level, agentic AI systems rely on the same machine learning technologies as generative AI, such as neural nets and GPUs. But instead of only producing pictures or text upon a prompt, an AI agent carries out a series of actions to reach a goal.
OpenAI's Operator was first announced in January 2025. Although it is still in its early stages, ChatGPT Pro users can test out and explore its ability to carry out tasks on the web.
Gumloop automates workflows using AI to carry out a variety of tasks, including web scraping and document processing.
GrowthLoop's Compound Marketing Engine uses agentic AI specifically to iterate marketing efforts for continuous growth. GrowthLoop’s Growth Agents are a team of AI-powered collaborators that work together, learn from your data, and suggest next-best actions that help marketers launch, optimize, and scale high-performing campaigns with ease.
Data Agent learns and understands the data in your cloud to provide user-friendly field descriptions while assessing what data would be most useful for each audience.
Audience Agent creates precise audience segments for your campaigns based on campaign history, traits, and attributes, and suggests additions to the segment as insights arrive.
Journey Agent builds personalized omnichannel journeys, choosing the optimal channels and timing based on historical performance data, and provides optimization options based on performance.
Insights Agent references historical and real-time performance data and helps other agents provide actionable recommendations for rapid improvement.
Research Agent acts as your personal brainstorming partner who searches the internet to retrieve contextual answers about campaign decisions.
Supervisor Agent oversees all other agents, making real-time assessments of when it's time to activate one or multiple agents to perform growth-driving actions for you.
Like other AI tools, AI agents can provide many benefits to businesses.
They are largely autonomous and adaptive, meaning that they can carry out routine tasks that work toward your goals without requiring constant monitoring or prompting. That said, a certain amount of human oversight is needed.
Agentic AI can help streamline your workflows by connecting processes in one platform to those in another. They act like virtual assistants that can take care of simple tasks.
AI capabilities also have the power to enable personalization at scale. They can help make sure that your message gets out to each lead, tailored precisely to their needs, using real-time data, without your team spending time digging for that information.
With their cross-modal capabilities, agentic AI can synthesize data across formats and databases to produce actionable insights. Like a human, they can go looking for information in different places, and draw conclusions based on the disparate things they find. But unlike a human, the agents can do this work in a matter of seconds.
AI agents can also remember context from previous interactions. This means that, like humans, their abilities can improve over time, becoming more accurate and relevant.
Most importantly, AI agents can elevate marketers’ work, eliminating tedious and time-consuming tasks while allowing teams to spend more time on high-value strategizing or campaign planning.
Before we dive into the use cases for using agentic AI in the customer journey, let’s cover some basics.
The customer journey can be split up into five separate stages:
Awareness
Consideration
Decision
Adoption
Advocacy
AI agents can influence each stage, impacting how businesses communicate with customers on a large scale. AI agents like those in the GrowthLoop Compound Marketing Engine can support the entire customer journey.
The Awareness stage turns people who know nothing about a brand into people who have some familiarity with it.
Typically, this stage involves many coarse-grained, widely spread marketing efforts. Examples include paid advertising, social media posts, and blog content. Since this is the very beginning of customer engagement, there is less information to do highly personalized outreach, although messaging can be tailored to broad demographics.
Agentic AI can help at this stage by uncovering potentially untapped audiences for paid advertising campaigns. For example, it could use data from previous successful campaigns and audiences to make recommendations for a new audience that you may not have reached with ads yet. It can also perform analytical tasks, such as gauging the success of various campaigns across different domains, based on performance data.
AI solutions may also help plan new marketing initiatives by analyzing available customer behavior information to construct useful and relevant journey recommendations.
During the consideration stage, potential customers conduct research to make the best decision. They often compare your product to competitors in terms of price, quality, and overall fit.
The main challenge of this stage is informing leads about your offerings, as well as crafting a clear and consistent value proposition that solves specific pain points. Customer interactions need to be fine-tuned and optimized around customer needs.
At this point, you may have acquired additional data about your leads who have interacted with your various channels and campaigns, which means AI can perform higher-resolution personalization in its outreach. For example, an AI agent might develop a campaign for a LinkedIn audience, recommending tailored messages based on prior interactions with the company’s content or channels.
This is when you finally make that first sale with a new customer. But the truth is, this is rarely the end of the journey. Instead, it's the beginning of a much longer customer relationship.
Since this stage is often a single moment, agentic AI doesn't have a huge role to play here. However, it can help personalize any messaging immediately after purchasing to make the customer feel more confident about their decision.
Customers who have purchased your product will start using it and forming their own judgments about its quality. The customer experience during this stage is largely shaped by the product itself, but interactions with your brand also play a crucial role. Quick and helpful customer support interactions can ensure that new customers have a positive impression of your company.
Generative AI is already making changes in businesses' abilities to carry out positive, personalized customer service interactions during this stage.
Agentic AI can take this a step further by autonomously performing email outreach. For example, checking up on new customers to ask about their experience and if they need help. Real-time information about their usage can often be used to trigger these interactions. Based on interactions like product use or website activity, AI can observe or predict the main challenges a new customer will have and proactively send them helpful information.
The best customers are long-term, loyal, and love your product so much that they promote it to their family and friends. Their brand loyalty will depend on their experience throughout their relationship with your company, and there are many touchpoints that can affect the customer experience.
AI agents can help with many of these touchpoints by proactively performing personalized outreach. For example, a subscription meal service might send individually personalized emails with recipe and nutrition information to each customer based on their prior purchase history.
Considering the importance of loyal customers, human and AI interaction is recommended. For example, if a customer replies with a complicated request, a human is better suited to provide the response.
Agentic AI is still a new concept and tool for many marketing teams. With that in mind, we recommend starting with a few small use cases as a test. Experiment and observe outcomes before determining the best workflows and processes to assign to the AI agents.
Depending on the task, an AI agent may be more likely to make an error than a human. But when they do, how is it reported and who is responsible? This question matters more depending on how important the task is, which is another reason why it is often best to use AI agents for simpler, low-consequence tasks to start. Avoid using agentic AI for highly important decision-making.
There is a common misperception that modern AI is like sci-fi AI, and is completely infallible, or even superior to human intelligence. In reality, all AI algorithms can make mistakes, often in ways that would be unlikely to be made by a human. You should check the output of any AI program you use on a regular basis for quality assurance.
Done improperly, agentic AI can feel insensitive, which might hurt brand image and customer satisfaction. Although it is an "intelligence," an AI agent is still a computer, and customers may pick up on that. The challenge is finding a balance between leveraging AI and maintaining relationships with customers.
AI programs can be biased, which is why it is crucial to invest in training and monitoring. Again, audit their behavior regularly, and respond swiftly to any complaints.
Like with generative AI, the rulebook is still being written. Although we will have a better sense in the years to come for how to use this technology, here are a few best practices to adopt in the meantime.
Be transparent about your use of agentic AI. You could potentially damage customer trust if they realize they're interacting with artificial intelligence — especially if they're misled into believing they are not. It's equally important to be transparent about any customer data used to power AI systems, and, when possible, offer customers the option to opt out to respect their privacy preferences.
Experiment widely, and observe carefully. AI agents are highly non-linear tools, and their behavior in one context may be very different from their behavior in another. Try many different tasks to evaluate where they excel for your business, and be decisive about discontinuing tasks that have less than acceptable results.
Be sure to balance human and AI interaction. Too much of either can create problems, but in the right proportion, they can work well together. Agentic AI and language models are great for proactively delivering personalized communications, but if and when a customer replies (especially with complex issues) it may be best to switch to human intervention.
Today's marketing cycle is painfully slow. Too often, businesses lose time assembling scattered data, have to design customer journeys from scratch, and lack the ability to personalize outreach at scale. There's a better way to do marketing, and it's emerging from new agentic AI technology.
GrowthLoop's Compound Marketing Engine works for you, as an extension of your marketing team, to optimize your communications and deliver consistently iterated and improved growth. Powered by your data cloud, it personalizes audiences and journeys, allowing you to achieve custom outreach at scale.
This is the fastest way to orchestrate personalized omnichannel marketing campaigns across your entire marketing stack.
Want to learn more about compound marketing? Take a look at our blog here.
