In recent years, the world of data science, and more specifically, artificial intelligence, has undergone a relentless evolution. In the second half of 2022, the global launch of generative AI marked an actual turning point, showcasing the potential of systems capable of generating coherent content, writing code, creating images, and even solving complex problems.
Today, AI is seen by businesses as an essential driver of competitiveness, pushing global investment to unprecedented levels. In Italy alone, there was a 58% increase in 2024 (according to the PoliMI Observatory), accompanied by a surge in practical applications and a significant improvement in their effectiveness.
In a world evolving on a daily basis, one of the most relevant topics is Agentic AI and AI Agents—an emerging paradigm that is redefining the boundaries of artificial intelligence. In this article, we’ll explore what Agentic AI is, how it differs from traditional solutions, and what its real-world implications are for any business.
Agentic AI and Adaptive Problem-Solving Capabilities
Given the dynamic and evolving nature of the topic, definitions of AI Agents (and Agentic AI) abound. In essence, they are software systems designed to autonomously carry out tasks in a manner that differs from traditional deterministic approaches based on progressively layered rule sets.
To autonomously achieve the goal for which they were developed—or with limited human supervision—AI Agents adopt an adaptive strategy. That is, they acquire the necessary information, identify the best course of action, and then carry out the strategy. This is complemented by self-learning capabilities that progressively enhance their problem-solving performance.
A concrete example of an AI Agent might be an advanced virtual shopping assistant that, after understanding a user’s preferences through provided information and purchase history, selects the ideal product, suggests it, completes the transaction, and tracks the shipment through to final delivery.
Taking a broader view, AI Agents can be described as autonomous cognitive systems, designed to solve problems and perform specific tasks through a decision-making process that closely resembles human reasoning, based on context perception, reasoning, and action.
AI Agents and LLMs: Strength in Synergy
To fully grasp the concept of Agentic AI, it’s important to clarify how it differs from Large Language Models (LLMs) such as GPT or Gemini. All LLMs are designed to generate responses based on input received from a user: when prompted, they engage a probabilistic generation process, drawing on knowledge gained during training. In other words, an LLM does not take initiative—it waits for a prompt, processes it, and returns an output.
AI Agents, as discussed, represent a shift toward automation and autonomy. They are not language models, but autonomous systems focused on problem-solving, executing complex tasks, or automating intricate business processes. That said, AI Agents and LLMs maintain a close relationship, as the agent’s action strategy is defined through interaction with an LLM (or even a Small Language Model—SLM), which becomes a foundational element of the agent’s architecture.
The Four Components of Agentic AI
With the right level of abstraction, the architecture of an AI Agent becomes easier to understand. It consists of four fundamental components.
Planning
Agentic AI learns its objective and breaks it down into a list of manageable sub-tasks using the reasoning capabilities of LLMs, in a way similar to the "chain of thought" method used in prompt engineering. The AI Agent analyzes each task, identifies the necessary resources, and proceeds through the various phases in a logical order.
Access to Custom Knowledge (RAG)
To achieve its goal, the agent can tap into knowledge beyond the LLM’s training data. Using a technique known as Retrieval-Augmented Generation (RAG), AI Agents can draw from secure, customized knowledge bases, enabling more accurate and reliable actions.
Tools
A key element of an agent’s architecture is its ability to interact with external tools, which is essential for extending operational capabilities. By connecting to tools like browsers, databases, third-party APIs, and enterprise systems, the agent can gather information, conduct web searches, and interface with external platforms, allowing it to tackle a wide variety of tasks.
Execution
Once the strategy is planned and information gathered, the agent moves on to execution, which may include generating reports, sending emails, managing orders or returns, closing contracts, or even triggering workflows across multiple virtual agents. The more complex the task, the greater the human supervision—though the aim is to reduce this over time.
A New Boost Toward Hyperautomation
AI Agents represent a major opportunity for companies long focused on optimizing and automating their internal and external processes. In this context, Agentic AI becomes a key enabler of the hyperautomation trend, which aims to automate as many business and IT processes as possible by leveraging the continually evolving potential of artificial intelligence.
While Robotic Process Automation (RPA) has proven effective in automating repetitive and rigidly defined tasks, AI Agents offer a significantly higher level of adaptability. Thanks to their ability to make autonomous decisions—though still with human oversight in some scenarios—and dynamically adapt to changing operational contexts, these systems mark a major leap forward in intelligent automation.
Real-world Applications, from CX to Logistics
One of the business areas likely to benefit most from the adoption of AI Agents is customer experience. These expert systems don’t just respond to support requests—they can autonomously solve real problems, a task that until recently was the exclusive domain of human operators. Thanks to their ability to analyze customer needs, AI Agents can make instantly actionable decisions, bringing the virtual experience closer to the in-store one.
This type of automation is applicable across almost every department. For example, in production and logistics, AI Agents can assist with planning by interacting with procurement systems to autonomously order materials. They can also monitor delivery routes and optimize them in real time, cutting transit times and improving operational efficiency.
In administrative settings, AI Agents could handle tasks such as monitoring incoming and outgoing international invoices entirely autonomously, verifying their accuracy against existing contracts and relevant regulations. This would significantly accelerate all subsequent phases of the billing cycle, including payment processing.
Agentic AI is not just a technological advancement—it’s a turning point on the road to deeper digitalization and smarter automation. It’s no coincidence that companies are channeling major investments into this area, fully aware that artificial intelligence is no longer a futuristic concept, but a real, present-day force capable of generating tangible value. And its impact is only set to grow.
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