As the surge in AI adoption continues to bolster and grow, there has been a level of confusion that persists, especially around “agentic AI.” Many people use the term “agentic AI” now, but most are failing to comprehend its meaning. This initial miscommunication has only been further amplified through repetition, resulting in many people believing that “agentic” is synonymous with “AI that works like an agent.” The term has essentially been watered down to mean “better chatbots” or “smarter self-service.” However, in reality, that’s not what agentic AI does, nor why it’s referred to by that title.
Rather, “agentic” stems from the AI being an active player rather than a passive one; it has agency in its own choices. As such, agentic AI makes decisions and takes action on its own, capable of far more than simply engaging in online conversations. Agentic AI can analyze complex data, predict behavior, and take action, all without ever necessitating explicit instructions to do so. This shift has major implications for customer service, automation, and AI-driven decision-making across industries, which is why it’s crucial to right the ship and reshape public perception of what agentic AI truly is.
What Is Agentic AI?
Agentic AI is any AI that can act with autonomy, not just assist. In this way, the distinction between traditional automation is massive. Even conversational AI is specifically engineered to respond based on prompts from the human user, just under the guise of a more natural conversation rather than input commands. By contrast, agentic AI works of its own accord, without the need for human input at all.
Its capabilities include analyzing data, forming hypotheses, testing solutions, and continuously improving without explicit human prompts.
Common Misconceptions Slowing Down Adoption
The term “agentic AI” has led many to mislabel it as an advanced chatbot, misassociating the term “agentic” with an agent, as in a representative. However, to perpetuate this mistruth is to severely underestimate the infrastructure and organizational change that agentic AI can bring. There has long been a need for enterprises to get their data in order, and agentic AI can do that and break down these established, siloed systems. In fact, businesses can increase their output tenfold with only 80% of the resources by properly leveraging agentic AI.
A Case Study on Agentic AI
One company exploring the practical applications of agentic AI is UJET, a customer service platform that integrates advanced AI into contact center operations.
Founded in 2015 and launched in 2017, UJET modernizes customer service through mobile-first, context-aware solutions. In 2018, Google Ventures invested in UJET, and the two companies now co-develop an AI-powered contact center solution. UJET’s approach stems from a foundational insight: “The landline phone is probably obsolete as a baseline for software design.”
UJET envisions AI evolving into autonomous problem-solvers, overcoming adoption challenges, and eventually enabling personal AI assistants to proactively manage user tasks and experiences.
As Baker Johnson of UJET says, “Agentic AI is going to be operational in nature. It’s going to be an order of magnitude increase in business capacity.”
The case of UJET illustrates how companies are starting to lay the groundwork for agentic AI – not just with new tools, but with reimagined infrastructure, data systems, and user experiences. It’s an evolving journey, but one that signals how agentic AI could reshape operations in industries far beyond customer service.
Final Thoughts
Agentic AI isn’t merely on the horizon – it’s already here, just misunderstood. Forward-thinking companies willing to restructure and embrace true autonomy in AI will lead the next wave of enterprise innovation. With agentic AI, success depends on more than algorithms: it requires strategy, data readiness, and cultural transformation.
