Functionality, fields of application and future developments of Agentic AI
Agentic AI: represents a revolutionary paradigm in the field of Artificial Intelligence, configuring itself as a system capable of achieving specific goals with limited human supervision. This system is composed of AI Agents, which are machine learning models that emulate the human decision-making process for real-time problem-solving.
Unlike traditional AI models, which operate within rigidly predefined constraints and require constant human intervention, Agentic AI possesses autonomy, goal-oriented behavior, and dynamic adaptability.
The term “agentic” refers to the organizational capacity of these models, meaning their ability to act independently and intentionally. In a multi-agent system, each individual agent performs a specific sub-task necessary for achieving the overall objective, while their efforts are coordinated through sophisticated AI orchestration mechanisms.
FUNCTIONALITY
Agentic AI systems follow an operational process articulated in several sequential and interdependent phases.
First, the Agentic AI initiates its operations by collecting data from the surrounding environment through sensors called APIs. These are direct interactions with users, which ensure access to up-to-date information for data processing and analysis.
Once the data is acquired, the AI processes it to extract significant insights. Using natural language processing (NLP), computer vision, or other advanced AI functionalities, the system interprets user requests, detects complex patterns, and understands the broader operational context.
The AI establishes goals based on predefined parameters or user inputs, subsequently developing specific strategies for their achievement through decision trees, reinforcement learning, or advanced planning algorithms.
The system evaluates multiple possible actions, selecting the optimal one based on factors like efficiency and accuracy.
After the action selection, the AI proceeds to its implementation, interacting with external systems or providing direct responses to users.
Subsequently, the AI evaluates the results obtained, gathering feedback to improve future decisions through reinforcement or self-supervised learning mechanisms.
The final and crucial moment of the AI agent’s operation is called orchestration. It consists of the coordination and management of multiple systems and agents. Orchestration platforms automate AI workflows, monitor progress toward task completion, manage resource utilization, oversee data flow and memory, and administer error events.
FIELDS OF APPLICATION
Agentic AI finds transversal application in various strategic sectors, revolutionizing operational processes through intelligent automation and predictive analysis.
In financial trading, autonomous agents analyze stock prices and economic indicators in real-time to perform predictive analytics and automated transactions, optimizing investment strategies with superior speed and accuracy.
The automotive sector, autonomous vehicles leverage real-time data sources from GPS and advanced sensors to significantly improve navigation and ensure high standards of operational safety, dynamically adapting to environmental conditions.
In the healthcare sector, this technology further demonstrates its versatility through intelligent agents that continuously monitor patients’ vital parameters, adapt therapeutic recommendations based on the latest test results, and provide decision support to doctors through specialized chatbots.
The same continuous monitoring capability characterizes its application in the field of cybersecurity, where Agentic AI systems constantly monitor network traffic, system logs, and user behavioral patterns to identify anomalies indicative of vulnerabilities to malware, phishing attacks, or unauthorized access attempts. Operational efficiency also extends to supply chain management, where autonomous artificial intelligence streamlines processes through automation and optimization of operations, autonomously managing orders to suppliers and adjusting production schedules to maintain optimal inventory levels.
Finally, in customer service, autonomous agents complete this application landscape by solving complex problems without the need for human escalation and managing the entire end-to-end customer journey with high operational efficiency, ensuring a personalized and consistent user experience.
FUTURE DEVELOPMENTS
Agentic AI is currently in an embryonic phase of development, especially within the Italian innovation landscape.
Virtual reality platforms, industrial digital twins, and simulation environments will represent fundamental controlled ecosystems for training and validating AI agents before operational deployment. These digital tools will allow for testing complex scenarios, significantly reducing the risks associated with direct implementation in production environments. Agentic AI therefore configures a paradigmatic revolution in intelligent automation that transcends the mere evolution of generative AI.
A significant strategic criticality for the European market emerges, however: the growing technological dependence on the US market in the field of AI innovation. Data from the international startup census confirms this worrying trend, with 60% of innovative companies in agentic automation concentrated in the USA and 87% of venture capital investments destined for the North American market, highlighting a significant competitive gap for Europe in the artificial intelligence economy.
