AI FUNDAMENTALS

AI Agent

AI Fundamentals

Definition

An AI agent is an autonomous system that can plan actions, use tools (APIs, databases, email) and complete complex tasks without continuous human intervention. Unlike a chatbot that answers questions, an agent can execute a complete workflow: analyze an order, check availability, generate a quote, and send it to the client.

Related terms

AI Fundamentals

AI Copilot

An AI copilot is an intelligent assistant that works alongside the user, suggesting actions and automating repetitive tasks without replacing human control. Unlike an autonomous agent, the copilot requires confirmation before acting. Microsoft Copilot, GitHub Copilot, and custom enterprise copilots are examples of this 'human-in-the-loop' philosophy.

Automation & RPA

Workflow Automation

Workflow automation is the design and automatic execution of sequences of business activities. For example: when an order arrives, the system checks customer credit, verifies availability, generates the order confirmation, and notifies the warehouse. Tools like n8n, Zapier, and Make allow building complex workflows without writing code.

Automation & RPA

Intelligent Automation

Intelligent automation combines RPA with AI to automate not just repetitive tasks but also those requiring comprehension and decision-making. A 'classic' RPA bot copies data; an intelligent automation reads an invoice, understands its content, decides how to classify it, and registers it in the management system. It is the natural evolution of RPA for mature companies.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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