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, generating 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 Agentive 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.
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 contrast with traditional deterministic approaches based on a structured, rule-driven workflow.
To achieve their goals autonomously—or with minimal human supervision—AI Agents follow an adaptive strategy: they gather the necessary information, identify the best course of action, and then execute it. All of this is enhanced by self-learning capabilities, which gradually improve their problem-solving performance.
A practical example of an AI Agent could be an advanced virtual shopping assistant that, by understanding a user’s preferences through received data and purchase history analysis, identifies the ideal product, suggests it, completes the purchase, and monitors shipping until final delivery.
On a broader level, AI Agents can be described as autonomous cognitive systems designed to solve problems and execute specific tasks through a decision-making process similar to that of humans, built on perception, reasoning, and action.
To fully grasp Agentic AI, it’s essential to distinguish it from Large Language Models (LLMs) such as GPT or Gemini. LLMs are designed to generate responses based on user input: when prompted, they generate outputs by applying probability-based models to knowledge acquired during training. In other words, an LLM doesn’t take initiative—it waits for a prompt, processes it, and returns an output.
AI Agents, however, represent an evolution toward automation and independence. They are not language models but autonomous systems designed for problem-solving, executing complex tasks, or automating business processes. That said, AI Agents and LLMs are closely connected: the agent often defines its strategy by interacting with an LLM (or an SLM—Small Language Model), making it a core component of the agent’s architecture.
Understanding the architecture of an AI Agent helps explain how it operates. Here are its core elements.
The Large (or Small) Language Model serves as the cognitive core of the agent. It interprets inputs, generates language, reasons about tasks, and plans actions. However, an LLM alone is not enough: it requires memory, tools, and knowledge to act effectively.
The AI Agent learns its objective and breaks it down into smaller sub-tasks, leveraging LLM reasoning in a way similar to the “chain of thought” used in prompt engineering. The key difference: while prompt engineering requires the user to manually define the workflow, an AI Agent builds it autonomously—analyzing the goal, decomposing it into sub-tasks, identifying resources, and executing each step logically and sequentially. Flexibility and goal-oriented adaptability are the key elements that distinguish AI agents from traditional automations based on predefined workflows.
The agent retains what it has done, seen, or inferred, allowing it to make consistent decisions. This memory may be short- or long-term, explicit or implicit, enabling context retention and avoiding unnecessary repetition.
To meet its goals, the agent may need to access knowledge beyond LLM training data. A common technique is RAG (Retrieval-Augmented Generation): before generating a response, the agent performs semantic searches within an updated knowledge base (such as company databases, documents, or internal policies). Relevant results are then added to the prompt, allowing the model to provide contextualized, accurate responses.
RAG is one of the simplest and most widespread techniques for extending the capabilities of an LLM, but it is not the only one. More structured approaches exist, such as building advanced knowledge graphs that semantically model entities and relationships, or using vector databases for a more sophisticated management of information relationships. The choice depends on the complexity of the environment, the available data, and the level of autonomy required of the agent
A fundamental component of an agent’s architecture is the ability to interact with external tools, which is essential to extend its operational capabilities. The agent can connect to tools such as browsers, databases, third-party APIs, and other enterprise systems to gather information, perform online searches, and interact with external platforms—allowing it to tackle a wide range of tasks.
Moreover, agents are not monolithic entities: they can be designed to collaborate with one another, exchanging tasks, data, or instructions. This interoperability makes it possible to build ecosystems of specialized agents that coordinate to handle complex workflows and expand operational possibilities. Naturally, a multi-agent architecture comes with a much higher level of complexity compared to a single agent: it requires mechanisms for coordination, priority management, memory sharing, and flow monitoring across entities.
Once the strategy is planned and information gathered, the agent proceeds to execution, which may include generating reports, sending emails, managing orders or returns, closing contracts, or activating workflows across multiple agents. The more complex the task, the more human oversight may be required—though to gradually reduce it.
Understanding the architecture is the first step. Bringing an AI Agent into production, integrating it into a business environment, and ensuring its reliability require tools, methods, and specialized expertise.
Platforms and frameworks for creating AI Agents already exist, but the real challenge lies in designing agents that are useful, scalable, and secure, aligned with business processes and data.
This involves combining various components: an LLM, an orchestrator to manage tasks and memory (e.g., LangChain), a queryable knowledge base, and the ability to connect with external tools.
The process must start with defining the objective, move through the design of the perception model (what the agent needs to “see” and “know”), and end with the interaction cycle (how it makes decisions, updates memory, triggers external tools, produces outputs, and collaborates with other agents).
The risks are real: a poorly designed agent may provide incorrect answers, execute unwanted actions, be manipulated by users, or expose sensitive data. That’s why it’s essential to rely on experienced partners who understand not only design and implementation but also KPI definition, monitoring, and operational risk management.
AI Agents represent a major opportunity for businesses striving to optimize and automate processes. In this sense, Agentive AI becomes a cornerstone of the hyperautomation trend, which seeks to automate as many business and IT processes as possible by leveraging AI’s constantly evolving capabilities.
While Robotic Process Automation (RPA) has proven effective in automating repetitive, rule-based tasks, AI Agents bring a higher level of adaptability. Thanks to their ability to make autonomous decisions and dynamically adapt to changing contexts, they represent a significant step forward in intelligent automation.
Designing and implementing effective, efficient, and secure AI Agents requires expertise that goes well beyond knowledge of language models. It calls for a solid approach that combines cutting-edge technology, regulatory compliance, security, and proven results.
One of our core areas of expertise is Data & AI. Every day, we leverage our skills, experience, and a high-level technology ecosystem to guide organizations toward true digital transformation.
If you are considering adopting AI Agents in your processes, want to enhance ongoing projects, or simply wish to understand the tangible benefits Agentic AI can bring to your business, contact us: we’re ready to build a successful path together.