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What Is Agentic AI and Why Does It Matter?
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Agentic AI is undoubtedly the defining theme of 2025. As an avid innovator or entrepreneur, you might be curious about what agentic AI is and why it holds such significance. That’s precisely what we’ll cover in this blog.
What Is Agentic AI?
Understanding the concept of agentic AI can be approached from two angles:
- What are non-AI agents?
- What is non-agentic AI?
What are non-AI agents?
The English dictionary defines an agent as “a person or thing that acts or has the power to act.” The core concept revolves around the ability to “act.”
Surprisingly similar, a “software application” is defined as “a set of instructions, data, or programs used to operate computers and execute specific tasks.”
For a new concept to be valid, there must be examples of existing concepts that don’t fit this new definition, showing that the new concept genuinely describes something different.
In this case, we need to find examples of software that is not an agent. This implies software whose tasks are not considered actions. This is a highly theoretical scenario, as even the simplest program produces some output – a display, a returned value, or an interaction with the external world.
As a result, the term “agent” becomes somewhat redundant in the context of software. A non-AI agentic application is essentially just a software application, while a non-AI multi-agent application can be understood as a modular software system composed of multiple interacting components.
Essentially, any software application capable of performing actions can be considered a digital agent. Maybe that is why Merriam-Webster gives the 5th definition of “agent” to be: a computer application designed to automate certain tasks (such as gathering information online).
What is non-agentic AI?
Here, “AI” refers to Gen AI or Large Language Models.
Foundational Gen AI models (or LLMs) can generate human-like text. However, they lack the “power to act” – a key characteristic of an agent.
Hence, an agentic AI application is an LLM-utilizing application that can perform actions.
“What actions?” you might ask. This is easier to answer. Let the bot perform tasks. Examples include sending emails or messages, updating documents, and booking reservations. People intuitively refer to these external actions as “tools” that the Gen AI can utilize.
However, actions can also encompass intermediate steps. For example, using a search engine to enhance conversation responses or loading tweets via the Twitter API. These are considered actions because they compel the Gen AI application to interact with the external world beyond mere language manipulation, even if the final output remains text.
Some propose treating every atomic action as an agent, leading to agents like “Draft Professional Email” and “Sentiment Analyzer.” However, this brings us back to our earlier point: these actions primarily involve language manipulation and lack significant external interactions.
This leads to a blurring of the lines between agentic and non-agentic AI. Ultimately, there may be little meaningful distinction between the two; they are both simply software applications that leverage Gen AI.
Why Agentic AI?
The obvious answer is that we want the Gen AI application to act. However, this begs a deeper question: If all software applications can act, why use Gen AI?
Traditional software development relies on the transfer of knowledge from domain experts to software professionals:
In the Gen AI era, this chain is replaced by an LLM that can:
- Understand and break down natural language input into a sequence of actionable steps.
- Identify the appropriate tools to perform each action.
- Compose the final output in natural language.
Therefore, determining when and how to leverage Gen AI agents requires careful consideration of when and how this replacement makes sense.
Common arguments for this replacement include:
- Access to broad expertise: While Gen AI may not surpass the best human experts in every field, it provides readily accessible average expertise across a wide range of domains. Also, the development cycle for Gen-AI-based solutions is significantly shorter.
- Handling long-tail input variations: Traditional software often struggles to efficiently and reliably handle unexpected or unusual user inputs, particularly when dealing with natural language. Gen AI can more effectively address these “corner cases,” albeit at the cost of potentially reduced reliability in core problem solving.
- Gen AI excels in text-related (or unstructured) tasks: It is backed by Large Language Models.
Navigating the Agentic AI Transformation
For now, the term “agentic AI” may be more of a marketing term than a truly distinct concept. It might be more accurately described as “natural-language-driven software applications,” or simply “(intelligent) chatbots”.
The most critical aspect of the agentic AI transformation is to carefully evaluate the replacement of the human chain involving domain experts and software professionals (or the traditional code produced from it) with an LLM. A thorough understanding of the motivations and potential consequences of this shift is crucial for making informed decisions regarding the appropriate use of agentic AI.
