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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.
Shortly after OpenAI’s ChatGPT sparked an AI revolution, a powerful form of artificial intelligence emerged as a competitive essential for organizations: Agentic AI.
Agentic AI takes the features of generative AI and chatbots a step further by autonomously completing actions. AI agents can perform concrete tasks, like messaging a prospect or searching for leads, all on their own — and they will forever change how teams operate.
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 needing constant human guidance. An AI agent does not need to be explicitly programmed with code to carry out specific tasks. Instead, it learns from examples on what to do and collaborates with other agents to complete a full marketing or customer experience task.
At a deeper level, agentic AI systems rely on the same machine learning technologies as generative AI, including neural networks 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.
Many organizations have integrated AI agents into their product offerings, with new agents announced daily.
OpenAI's Operator was first announced in January 2025 and is now fully integrated with ChatGPT as ChatGPT agent. The tool is designed to perform various website tasks, like completing forms or placing orders.
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.
AI agents can provide many benefits to organizational teams — especially marketing and customer experience teams seeking to personalize experiences at scale, in real time.
AI agents are largely autonomous and adaptive, meaning they can carry out routine tasks that work toward your goals without requiring constant monitoring or prompting.
That said, human oversight is necessary; the 2025 AI and Marketing Performance Index found that 46% of marketers believe AI should only make low-risk decisions with human oversight, and 31% see AI as a recommendation engine that requires humans to make all final decisions. Respondents to Cisco’s agentic AI for CX research share this sentiment, with 89% believing teams must combine empathy and human connection with agentic AI efficiency.
Agentic AI can help streamline your workflows by connecting processes in one platform to those in another and preserving all customer data. An agentic tech stack connects customer records stored in a data cloud with all channel-specific tools, so outreach tracked in a CRM and feedback received through surveying platforms is reflected to fuel real-time decision-making.
A major benefit is that agentic AI enables personalization at scale, which is increasingly necessary to engage customers and preserve their loyalty.
Nearly 70% of consumers expect to receive personalized interactions from companies, and 76% even get frustrated by generic or mispersonalized messages. AI agents deliver on these expectations and ensure 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 cross-modal capabilities, agentic AI can synthesize data across formats and databases to produce actionable insights. Like a human, they find and synthesize 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.
At their core AI agents 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 to improve 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 available to orchestrate highly personalized outreach, although messaging can be tailored to broad demographics.
Agentic AI can help at this stage by identifying 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 informed decisions. 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 it’s 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. 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 or service 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.
Past research suggests that one in three customers will stop supporting a brand they used to love after just one negative experience.
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 at the right moment.
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 every touchpoint can affect the customer experience.
AI agents can help across 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 as you build your AI governance. Avoid using agentic AI for highly important decision-making or critical moments in the customer’s journey.
There is a common misperception that modern AI is like sci-fi AI; highly intelligent and always right (maybe 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 regularly check the output of any AI program you use for quality assurance.
Done improperly, agentic AI can feel insensitive or generic, 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.
Considering the challenges and pitfalls of agentic AI, there are several low-risk AI starting points to make immediate improvements in your customer experience before expanding to more complex use cases.
Customer journey maps are a vital tool to understand how distinct customer segments navigate their journey from discovery to post-purchase. AI can leverage your existing customer data to map the common journeys customers take and compare that data to the maps your team currently use.
Refreshed customer journey maps created with relevant data can fuel better targeting across the journey so your team focuses on the channels and touchpoints that matter most to customers. Agentic AI is especially helpful in suggesting the optimal customer outreach channels and suggesting messaging variations based on individual customer preferences.
Review your customer support avenues, such as a support hub, chatbot, frequently asked questions page, or documentation portal. Ask AI to assess if your existing avenues effectively address common customer needs and how you can improve your existing support ecosystem.
By providing customers with flexible and convenient support options, they can more quickly resolve their issues on their own, which decreases reliance on your human support staff and helps customers overcome possible hurdles with ease.
Agentic AI can also monitor user activities to identify if they may need help using the product or service, such as sending new customers how-to videos to help them navigate the interface, or sending experienced users information about lesser-known features that support their common use cases.
Tell AI to analyze your customer support transcripts, recent customer reviews, and social media conversations about your product or service. Ask your tool to categorize common issues by customer journey stage and identify the issues that are likely to have the greatest impact on moving customers from one stage to the next.
Agentic AI can also monitor for negative reviews or feedback and suggest targeted interventions to fix problems before a customer is lost for good.
Given rapid advancements in agentic AI, organizations are constantly finding new and better ways to integrate the technology into their workflows. 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.
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.
Traditional marketing cycles are 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.
Ready to evolve your team to an agentic marketing organization? Download our in-depth guide to learn how.
