Mar 19, 2025 Hannah Kuck
ShareHow Large Language Models Enable the Next Milestone in Digital Transformation
Imagine a digital assistant that not only understands what you say but also grasps what you mean – and executes it before you even finish your sentence.
This scenario is no longer science fiction but a vision becoming increasingly tangible thanks to the rapid advancements in artificial intelligence (AI). While still a glimpse into the future, work on making this vision a reality is already underway.
A key component of this development is the language comprehension of modern AI models. Only through advancements in natural language processing (NLP) can AI agents achieve the depth of understanding necessary to not only support but actively restructure business processes.
But what does this mean for companies? This article provides an introduction to the concept of Agentic AI, explores the fundamentals of this pioneering technology, and explains why AI agents are the key to automation, productivity, and improved decision-making across various industries.
The Concept of Agentic AI
This scenario exemplifies how Agentic AI might function in the future: AI systems that not only analyze data and make recommendations but also take autonomous actions to achieve strategic goals.
According to U.S. market research company Gartner, by 2028, 33% of enterprise software applications will incorporate Agentic AI, up from less than 1% in 2024, enabling 15% of daily business decisions to be made autonomously.
Large Language Models (LLMs) as Catalysts for Agentic AI
Language models such as OpenAI’s GPT series form the foundation of Agentic AI. These large language models (LLMs) are trained on vast amounts of text and exhibit deep language comprehension. Natural language processing (NLP) allows computers to understand, generate, and respond to human language.
LLMs recognize linguistic patterns, capture nuances, and can respond to natural language inputs. Their ability to incorporate contextual information, draw conclusions, and provide optimized responses allows them to go beyond executing simple commands.
These advanced language models are the key enabler of Agentic AI: Without understanding complex linguistic contexts, AI agents would not be able to make independent decisions or take action.
The Technology Behind LLMs
- From a technological perspective, LLMs rely on a combination of advanced machine learning and neural networks inspired by the workings of the human brain.
- A crucial component of neural networks is the Transformer architecture, which uses mechanisms to understand the meaning of each word in the context of an entire sentence.
Hierarchical classification of large language models (LLMs): LLMs are a subset of transformer models that are based on neural networks and are assigned to machine learning.
The Vision of a Human-AI Symbiosis
To recap: Agentic AI refers to advanced AI systems that not only respond to human input but also make autonomous decisions, solve problems, and pursue long-term goals.
But how exactly does this autonomy become possible? Two essential mechanisms play a crucial role:
- The Observation-Thought-Action Loop: AI agents analyze situations, plan actions, and execute decisions independently.
- Tool Interaction: AI agents interact with software, databases, or APIs to expand their capabilities and seamlessly support business processes.
The Observation-Thought-Action loop is a step-by-step process from input to output, in which an iterative cycle of thinking, acting and observing leads to the final output.
These mechanisms elevate human-AI collaboration to a new level: Humans define goals, while AI agents autonomously determine the best paths to achieve them.
Interaction between users and large language models: The models capture the user's intention and use appropriate tools for support.
From Tool to Partner
The transition from AI as a mere tool to autonomous agents marks a revolutionary shift. While traditional systems react to instructions, AI agents make independent decisions and act autonomously.
Example:
- Chatbots interact with users, answer questions, and perform simple tasks – but only react to inputs.
- Agents take on complex tasks, make decisions, and adapt to new situations – for example, an AI assistant that autonomously plans an event, gathers quotes, and communicates with service providers.
This transition fundamentally changes human-machine interaction: AI systems become active partners, elevating automation and intelligent decision-making in businesses to a new level.
Conclusion and Outlook
Agentic AI has the potential to transform businesses by autonomously optimizing tasks. However, the technology is still in its early stages and comes with ethical, security, and technical challenges.
To harness its potential responsibly, robust security strategies, ethical guidelines, and continuous adaptation are essential. The future promises significant advancements and new applications.
Companies should actively monitor developments to seize opportunities early and drive sustainable innovation.
Would you like to learn more about how we at INFORM use cutting-edge AI technologies to optimize complex business processes?
About our Expert
Hannah Kuck
Corporate Communications Manager
Hannah Kuck has been working as Corporate Communications Manager in Corporate Marketing at INFORM since August 2024. With a passion for creative and effective communication, she helps shape various areas of corporate communications - from press relations to content creation and storytelling.