Every time we think we understand the capabilities of AI and which future trends the technology is about to inspire, a new breakthrough emerges with even greater potential to transform an element of our professional or personal lives. The latest development with boundary-pushing possibilities is agentic AI, which we expect to be one of the hottest topics of discussion and debate over the course of 2025.
The discussion will stem from its ability to operate with an element of autonomy — i.e., without the need for direct human input or monitoring. And the debate will be due to the potential ethical and security implications of allowing this type of technology to operate within an organization without oversight, transparency of reasoning or supervision.
What is agentic AI?
The clue is in the name because agentic AI has ‘agency.’ Now, while agentic AI isn’t sentient, what it can do, thanks to its ability to leverage machine learning, natural language processing (NLP), and automation technologies, is essentially assess and evaluate the situation and determine a course of action based on a combination of operational parameters and learned experiences to date.
The technology is best understood as being a framework or architecture in which AI agents can function. Each of these agents can be assigned a different task to achieve a wider or more complex objective. If we think about something as complicated as a self-driving or semi-autonomous car (particularly one with an internal combustion engine rather than a simple electric motor), accelerator position, gear change, steering input, navigation, the application of brakes and the comprehension of traffic conditions are each individual tasks. But collectively they come together to realize a single goal — allowing a car to drive without human input.
How does agentic AI differ from generative AI?
What agentic and generative AI both have in common is that they exist thanks to advances in the same technologies — large language models (LLMs), NLP and machine learning. What sets them apart is what they achieve through those technologies.
For all of generative AI’s capabilities, particularly in areas such as content, image or even code creation, it can achieve nothing without a direct human prompt, and what is created either resembles or is inspired by that prompt and the data used to train the system.
GenAI is reactive. It is focused on delivering an output following an instruction to do so, whereas agentic AI is active and focused on execution. It can undertake a task (including generating content) and act with a level of autonomy without direct prompting or inputs.
As well as a level of autonomy that enables it to assess situations and determine actions based on its understanding, agentic AI can adapt. As an agent starts undertaking a task, it learns from the experience of its execution, as well as from feedback from other sources, and adjusts its performance over time.
The final characteristic is its goal-orientated behavior. It can be deployed to discover and then replicate the optimum way of performing an action to deliver the best outcome. And this becomes a very powerful capability when a team of AI agents each identify an optimum means of delivering individually and then collectively.
Agentic customer experience
With so much potential, it should come as little surprise as to why there is so much excitement about what agentic AI could do, especially in areas such as customer experience delivery.
Greater personalization
Agentic AI’s ability to learn from past experience and make decisions based on accumulated knowledge means it can be deployed to offer tailored customer recommendations or to structure personalized responses to issues, in real time. Using existing customer data for training purposes also means that it could optimize marketing campaigns.
Next-level self-service
Existing capabilities in the field of AI have taken chatbots and voicebots to a new level, but with agentic AI those self-service options can start resolving complex issues or take action — such as generating a ticket or creating and closing cases for resolution, without having to escalate the issue to a live agent.
Outbound sales success
Agentic AI’s interpretive skills mean it could be programmed to trawl through databases, CRMs, email chains or other communications to identify leads for outbound agents focused on sales, and to help those agents hone responses to questions or increase the chances of successfully closing the sale.
Optimized operations
Its ability to adapt to change and achieve goals also means it can optimize workflows and resource allocation and assign tasks or cases based on real-time needs. This increases both efficiency and productivity and, like all the best applications of technology within the CX space, enables customer-facing staff to focus on issues where there is the greatest possibility of adding value or delivering an emotional as well as technical resolution.
Increased capacity
Agentic AI can perform a task at a granular level and groups of specialist AI agents can be pooled to work in unison to cover every element of a complex function or undertaking. In other words, groups of these agents could be used as extra resources to cover staff shortages.
What are the ethical implications?
But of course, as with all other types and applications of artificial intelligence, these capabilities also raise ethical issues. And one of the biggest is accountability. If it makes a mistake or chooses a course of action that could potentially cause harm, who is responsible?
There are also risks associated with the training data. Quality of input influences quality of output and agentic AI can still suffer from unconscious bias or a lack of fairness and, because it has the agency to make decisions, the potential impacts could be greater.
As with other types of AI, there is also the black box issue. Without transparency of operation, there is no way to reverse engineer or understand how an AI agent arrived at a particular decision or course of action. This lack of transparency could be the biggest obstacle to trust and widespread adoption.
Its ability to autonomously manage complex workflows will potentially turbocharge productivity, but it also raises questions and further stokes fears about job displacement. Organizations are going to have to be clear about how they intend to deploy the technology.
And finally, there are also potential security risks regarding the technology. It could be hacked or misused to intentionally cause harm to an organization. And again, returning to the point of autonomy, how do you ensure such systems are secure and trustworthy?
Is there a best practice for harnessing agentic AI?
To unlock the technology’s potential while offsetting its ethical implications, particularly in an area like customer experience — the organizational function that defines its brand and value proposition — demands a clearly defined plan.
1. Guidelines
Start with a framework that outlines the boundaries and parameters in which the technology will operate. Choose an area or operation that is easy to control, where there is little risk of overlap or contamination across other functions, and where it is easy to apply metrics or other means of assessing performance. Starting small will also keep governance simple and will make it easier to align with your organization’s ethical standards.
2. Collaboration
To succeed, any pilot project or proof of concept needs support or backing from the wider organization. Therefore, actively seek buy-in before any project commences. Likewise, due to concerns about job displacement, make certain that employees understand not just the scope of the project, but the motivations behind it. Even if agentic AI has the potential to automate processes and even entire roles, it will function best when deployed alongside employees. No one understands a role within an organization better than the person in that role, and each employee’s insights and support could be critical in a successful implementation.
3. Clarity
Agentic AI needs to be transparent and its actions understood by humans. So, implement measures to ensure the technology’s decision-making process can be analyzed, monitored and replicated. Without transparency, it will be impossible to completely trust or accept the technology. As projects move towards maturity, that transparency will need to be operationalized in the form of governance or a steering committee.
4. Engagement
As well as building consensus and achieving buy-in for any project, any implementation needs to be supported through engagement with the wider organization, customers and regulatory bodies. This will provide a more diverse and comprehensive set of perspectives and will help in the development of more robust applications and solutions.
5. Education
Several months ago, no one had heard of agentic AI. Two years ago, ChatGPT didn’t exist. The field is changing so quickly that your organization needs to keep its finger on the pulse through continuous learning and research. And, as well as keeping track of what’s possible and therefore what could influence or change the outcome of existing pilot projects or implementations, it’s critical to maintain a focus on employee training and education. The people within your organization are going to need to adapt to changes in technology and will need to feel confident in using new tools and processes.
A little over two years ago, we were discussing generative AI’s potential to transform the way we work. And today, the capabilities of agentic AI could push things even further. And it’s because of those capabilities that organizations need to treat the technology with caution as well as curiosity.
By understanding what agentic AI is, recognizing its potential applications in customer experience delivery, and addressing the ethical implications it raises, businesses can harness its capabilities to enhance innovation, improve efficiency and ultimately create value for all stakeholders. Embracing agentic AI responsibly will not only pave the way for technological advancement but also ensure that the human element remains central.
Agentic AI is certain to shape the customer experience trends of 2026, but to discover how technology is driving innovation in CX now, read our “2025 CX trends report: From buzzword to business case.”