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Artificial intelligence (AI) is the broad category; generative AI is one kind of AI, retrieval-augmented generation (RAG) adds information from a separate source at answer time, and an AI agent may take actions rather than only produce an answer. These terms describe different things—and, especially for “agent,” their meanings are not always settled.
Artificial intelligence (AI)
AI is a broad term for machine-based systems that perform tasks such as producing predictions, recommendations, or decisions. There is no single definition used for every purpose: NIST’s AI glossary lists multiple definitions from different source documents, including formulations focused on tasks associated with human intelligence and systems that act rationally.
When you encounter “AI,” check the context. A technical document, a policy discussion, and a law may use different definitions, so the word alone does not tell you what a particular system can do.
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Generative AI is a class of AI models that generates derived synthetic content. NIST’s GenAI glossary entry describes models that emulate the structure and characteristics of input data. The outputs can include text, images, video, audio, and other digital content.
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“Generative” names a capability, not a synonym for AI as a whole. An AI system that makes predictions or recommendations need not generate content.
Retrieval-augmented generation (RAG)
RAG combines a generative model with a separate retrieval system or knowledge base. When a user asks a question, the retrieval component finds relevant information and supplies it to the model as context for its response. NIST’s RAG glossary entry describes this information flow.
Because the retrieved material is separate from the model’s learned parameters, the information supplied at answer time can be changed without retraining the model. That mechanism does not guarantee a correct answer: the response still depends on what is retrieved and how the model uses it.
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AI model and AI system: what distinguishes them?
A model is the component that has learned patterns from data and can infer or generate outputs. A system is the broader machine-based arrangement that uses a model and may include other components, such as software, data sources, or tools. A model can therefore be part of an AI system; the two terms are not interchangeable.
That distinction also appears in law, but legal definitions are specific to their jurisdiction and purpose. Under the EU AI Act, an AI system is a machine-based system designed to operate with varying levels of autonomy and that may adapt after deployment. It infers from inputs how to generate outputs—including predictions, content, recommendations, or decisions—that can influence physical or virtual environments. The European Commission AI Act Service Desk’s Article 3 page says its text reflects the consolidated version dated 27 July 2026. This is the Act’s definition, not a universal technical definition of AI systems.
AI agent and agentic AI
An AI agent generally receives and processes information from an environment and executes actions that can affect that environment. An agentic AI setup emphasizes behavior directed toward objectives, often involving outputs or actions and some degree of autonomy. The terms are evolving rather than precisely standardized.
The European Commission AI Act Service Desk says the term “AI agent” is often used inconsistently and is not legally defined in the AI Act. Its FAQ puts the issue this way: “The term ‘AI agent’ is often used inconsistently in public debate, due in part to a blurry and still evolving demarcation between AI agents and other kinds of AI.” An OECD paper, The agentic AI landscape and its conceptual foundations, published 13 February 2026, likewise finds that objectives, outputs—often actions—and autonomy are common elements across definitions, while the formulations differ.
In practice, the useful question is what the system is configured and authorized to do. Producing a response is not the same as taking an action that changes an environment; the label “agent” alone does not establish how much autonomy or authority a particular system has.
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| Term | What it describes | Key distinction |
|---|---|---|
| AI | A broad category of machine-based systems | Definitions vary by source and context. |
| Generative AI | A class of models that generates derived synthetic content | A subset of AI, not all AI. |
| RAG | A setup that supplies retrieved information to a generative model at query time | External information can change without retraining the model; correctness is not guaranteed. |
| AI model | A component that learns patterns and produces inferences or outputs | Can be used within a larger AI system. |
| AI system | A broader machine-based arrangement; under the EU AI Act, a specifically defined legal category | The legal meaning applies to that regulation, not universally. |
| AI agent / agentic AI | Terms for AI setups associated with objectives, environmental inputs, and actions | Usage is evolving and inconsistent; a label alone does not establish autonomy. |
Further terminology reference
For a more extensive terminology reference, NIST published The Language of Trustworthy AI: An In-Depth Glossary of Terms on 29 March 2023. It is a glossary resource, rather than a claim that every term has one universally accepted definition.
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