Agentic AI: Between Conceptual Precision and Hype-Driven Misuse

"Agentic AI" is among the terms that have likely gained popularity the fastest in recent months. It is used almost inflationarily in specialist articles, press releases, and pitch decks—as a synonym for progress, autonomy, and the next evolutionary stage of artificial intelligence. But what actually gives a system the qualities of an “agent”? And which technologies truly deserve this designation?

In the current debate, Agentic AI is often confused with conventional automation technologies. Even companies that rely solely on rule-based assistance systems or predefined decision logic promote their products under the label of “Agentic Intelligence.” In some cases, a simple plugin or an automated recommendation in a well-known streaming service is enough to be presented as an example of Agentic AI in marketing contexts—a development that is problematic from both a scientific and technological perspective.

The current discussion evokes memories of earlier hype cycles in the field of artificial intelligence, during which terms like “deep learning” or “neural networks” were overused and eventually lost their meaning. Even then, there was a significant gap between ambitious expectations and actual functionality. In the case of Agentic AI, a similar conceptual vagueness now looms—potentially leading to far-reaching consequences for strategic business decisions, investments, and trust in technological innovation.

Yet the term is by no means arbitrary. An agentic system differs fundamentally from traditional automation solutions: it does not simply operate along predefined workflows but makes independent decisions based on context, goals, and continuous feedback—without requiring human intervention at every step. It learns in real time, dynamically adjusts its strategy, and is ideally capable of interacting with other agents to collaboratively solve complex tasks.

What truly defines agentic AI and why many systems fall short of this label

Agentic AI is not a marketing concept, but a fundamental shift in the relationship between a system and its environment. At its core lies not just the ability to automate, but the capacity for adaptive, context-aware autonomy. An agent does not pursue a goal by following a fixed sequence of steps; rather, it acts through independent interpretation, prioritization, and decision-making. It can question strategies, choose new paths, develop alternative approaches, and respond to unexpected changes in its environment—all without external assistance. Real-time learning, goal-directed action under uncertainty, and the ability to interact with other autonomous systems are key characteristics of such an agent.

It is not a question of whether humans are still involved, but when and in what capacity. Agentic AI does not imply the complete absence of human oversight. Rather, it calls for intelligent integration of control—not at the level of operational confirmation, but in terms of strategic supervision and ethical governance. An agent does not need to operate in complete isolation to be considered agentic; however, it must not depend on constant guidance or approval.

Despite these clear conceptual boundaries, the term is currently applied to systems that fail to meet these requirements. Many applications that merely deliver rule-based responses or accelerate predefined processes now label themselves as “agentic.” This is especially common in the field of generative AI, where distinctions are becoming increasingly blurred. A language model that reacts to input is frequently—and incorrectly—presented as an “autonomously acting agent,” despite lacking any genuine goal orientation or the ability to respond strategically to changes in its environment. Even where elements of decision-making are present, the capacity for long-term self-correction or negotiation of complex goal conflicts is often missing.

Overextending the concept of agency is not merely a semantic issue—it presents serious strategic risks. Companies that place their trust in supposedly agentic systems without verifying their actual functionality risk critical misjudgments. Decisions about technological infrastructure, investment, or workforce planning may rest on assumptions that are not borne out by the product itself. When promises and real capabilities diverge, the long-term effect is a loss of trust in AI technologies. While the “Agentic AI” label might capture short-term attention in marketing, it is misleading from a strategic perspective.

In this context, it becomes essential for decision-makers to rely on solid criteria and reference points. It is not the terminology in a pitch deck that provides orientation, but functional analysis:

• Does the system operate within rigid rules, or does it possess an internal model of goals?

• Does it merely react to input, or is it capable of taking initiative?

• Is human reframing the only way to implement change, or can the system adjust its strategies independently?

• And finally: how crucial is human oversight—supplementary, or foundational?

  
  

While such questions rarely allow for simple yes-or-no answers, they form the basis of a methodical approach that helps organizations distinguish between genuinely agentic systems and overhyped automation. Especially in a time of technological upheaval, this distinction is critical—not only for selecting appropriate solutions, but for preserving trust in the broader application of artificial intelligence in enterprise environments.