Commission guidance on the AI system definition

The Commission's first interpretive Communication on the EU AI Act addresses the definition of an AI system in Article 3(1). It was adopted on 29 July 2025 as Communication C(2025) 5053 final. This page paraphrases the Commission's seven-element framework and the carve-outs the Commission identifies for systems that fall outside the definition.

Status

Commission interpretive guidance, not binding law. The EU AI Act text and any Court of Justice of the European Union (CJEU) interpretation prevail in case of conflict. The Commission itself states this at paragraph (7) of the Communication.

Quick decision

• You want to confirm your system meets the Article 3(1) definition → work through the seven elements below. The Commission's load-bearing test is element 5 (inference).
• You think your system is rule-based or basic data processing → see Systems outside the AI-system definition — the Commission identifies four categories of carve-outs at paragraphs (41)–(51).
• Your system has frozen weights and does not learn after deployment → that does not exclude it from the definition. Per Commission paragraph (23), adaptiveness is optional ('may').
• You need to know whether your system is also a general-purpose AI model → the AI-system definition (Article 3(1)) and the GPAI model definition (Article 3(63)) are distinct. See the General-purpose AI models page.
• You want to know whether your system is high-risk → the definition is the entry test; downstream classification under Articles 5 and 6 then determines the obligations regime. See the draft high-risk classification guidance.

TL;DR

• The Article 3(1) definition has seven elements per Commission paragraph (9): machine-based; autonomy; adaptiveness; objectives; inference; outputs; environmental influence.
• Inference is the load-bearing element — the Commission treats it as the principal distinguishing characteristic between AI systems and traditional software (paragraph (26)+).
• Adaptiveness is optional (paragraph (23)). Frozen-weight systems are not excluded merely because they do not adapt after deployment, if the other Article 3(1) elements are met.
• The Commission identifies four categories of systems outside the definition at paragraphs (41)–(51): mathematical-optimisation systems, basic data processing, classical heuristics, simple prediction systems.
• The definition is system-level, not model-level — the model, pipeline, interfaces and environment are all part of the AI system being assessed.
• The Commission emphasises a lifecycle perspective (paragraph (10)): the seven elements need not all be present simultaneously across building and use phases.

Primary source

Commission Communication C(2025) 5053 final, Brussels, 29 July 2025 — Commission Guidelines on the definition of an artificial intelligence system established by Regulation (EU) 2024/1689 (AI Act). © European Union. AI Act Service Desk PDF. Adopted under Article 96(1)(f) AI Act.

The seven elements of Article 3(1)

Article 3(1) AI Act defines an AI system as follows (per the OJ-published text and quoted in Commission paragraph (8)):

‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

The Commission decomposes this into seven elements (paragraph (9)). They are read together; the lifecycle perspective at paragraph (10) means not every element has to be present in every phase.

Element 1 — Machine-based system (paragraphs (11)–(13))

The Commission reads 'machine' broadly. Both hardware components (processing units, memory, storage, networking, I/O interfaces) and software components (code, instructions, programs, operating systems, applications) count (paragraph (11)). The Commission expressly states at paragraph (13) that emerging quantum-computing systems are machine-based despite their unique operational principles. The 'machine-based' element underlines that the system must be computationally driven (paragraph (12)) — it does not narrow scope to any particular architecture.

Element 2 — Designed to operate with varying levels of autonomy (paragraphs (14)–(21))

The Commission reads 'autonomy' as a spectrum (paragraph (14)). A system can have some autonomy if it can generate an output without that output being manually controlled or exactly specified by a human (paragraph (18)). The Commission explains that Recital 12 excludes systems designed to operate solely with full manual human involvement and intervention (paragraph (17)); full autonomy (no human supervision) is not required for the definition to be met.

The implication is operational: the autonomy element is satisfied for almost any production-deployed system that does not require step-by-step human approval of every output. Excluding a system on autonomy grounds requires showing full manual human involvement and intervention at every operational step.

Element 3 — May exhibit adaptiveness after deployment (paragraphs (22)–(23))

The Commission is explicit at paragraph (23) that the word 'may' indicates a system does not necessarily have to be capable of adaptiveness. Frozen-weight models that do not learn from new inputs after deployment still meet the definition if the other elements are present.

This is one of the most frequently misread parts of Article 3(1). Adaptiveness is an optional characteristic; absence of adaptiveness is not a basis for excluding a system from the definition.

Element 4 — For explicit or implicit objectives (paragraphs (24)–(25))

The Commission reads 'objectives' broadly to include both objectives the developer encodes explicitly (e.g. a loss function, a stated business outcome) and objectives that emerge implicitly (e.g. learnt from training data without a stated specification). The Commission emphasises that the objectives element is about the system's purposive behaviour, not about the developer's documented intent.

Element 5 — Inference, from input it receives, how to generate outputs (paragraph (26)+)

This is the Commission's load-bearing element. Per paragraph (26), the system must be able to infer — meaning to derive outputs from inputs using techniques that go beyond basic data processing or rule-based execution. The Commission spends roughly a third of the entire guideline (paragraph (26) through paragraph (51)) on what counts as inference and what does not.

The Commission's read at paragraphs (30)–(39) is that AI techniques enabling inference include machine-learning approaches (supervised, unsupervised, self-supervised, reinforcement learning, and deep learning) and logic- and knowledge-based approaches (knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, symbolic reasoning, expert systems, and search and optimisation methods).

The element 5 test is the principal screen that determines whether a system is in scope. The carve-outs in paragraphs (41)–(51) are all about systems that have a narrow capacity to infer but do not cross the inference threshold the Commission considers material — see the next section.

Element 6 — Outputs (predictions, content, recommendations, decisions) (paragraph (52)+)

The Commission reads 'outputs' broadly. The four categories listed in Article 3(1) — predictions, content, recommendations, decisions — are non-exhaustive (paragraph (52)+) and span most operationally meaningful AI-system outputs. Notably, the Commission treats content generation as a first-class output category, putting generative AI systems squarely within the definition.

Element 7 — Can influence physical or virtual environments (paragraph (60)+)

The Commission reads 'environment' to include both physical environments (e.g. robotic actuation, autonomous vehicles, medical-device control) and virtual environments (e.g. user-interface decisions, content recommendation, AI-generated content). The 'can influence' phrasing is significant: the system does not need to actually influence an environment at all times; the capacity to do so is sufficient (paragraph (60)+).

Systems outside the AI-system definition

The Commission identifies four categories of systems that may have a narrow capacity to infer but nevertheless fall outside the Article 3(1) definition because they do not transcend basic data processing (paragraph (41)).

1. Mathematical-optimisation systems (paragraphs (42)–(45))

Systems used to improve mathematical optimisation or to accelerate traditional, well-established optimisation methods are excluded. The Commission's examples:

• Physics-based simulations (paragraph (43)) — machine-learning approximations of cloud microphysics, turbulence or other atmospheric processes used to speed up traditional physics models.
• Telecommunications optimisation (paragraph (44)) — machine-learning prediction of network traffic to allocate satellite bandwidth.
• Traditional linear / logistic regression methods and ML-based optimisation approximators (paragraph (42)) — when used to accelerate or approximate well-established optimisation methods without transcending basic data processing.

The Commission's reasoning at paragraph (45) is that automatic self-adjustments in these systems are aimed at improving computational performance of the underlying method, not at adjusting the system's decision-making model in an intelligent way.

2. Basic data processing (paragraphs (46)–(47))

Systems that follow predefined, explicit instructions or operations — without learning, reasoning or modelling at any stage of the lifecycle — are excluded. The Commission's examples:

• Database management systems used to sort or filter data (e.g. "find all customers who purchased a specific product in the last month") (paragraph (46)).
• Standard spreadsheet software without AI-enabled functionalities (paragraph (46)).
• Software that calculates a population average from a survey (paragraph (46)).
• Software for sales report visualisation using statistical methods to create dashboards (paragraph (47)) — does not recommend how to improve sales or which products to promote.
• Software applying statistical techniques to opinion polls to determine validity, reliability, correlation or statistical significance (paragraph (47)).

The Commission's reasoning is that these systems do not 'learn, reason or model' — they present data in an informative way.

3. Classical heuristics (paragraph (48))

Rule-based, pattern-recognition or trial-and-error problem-solving techniques that do not rely on data-driven learning are excluded. The Commission's canonical example is a chess program using a minimax algorithm with heuristic evaluation functions — it assesses board positions without requiring prior learning from data.

4. Simple prediction systems (paragraphs (49)–(51))

Machine-based systems whose performance can be achieved via a basic statistical learning rule are excluded due to their performance. The Commission's examples:

• Financial forecasting using a mean-strategy estimator to predict future stock prices from the historical average (paragraph (50)).
• Temperature forecasting using last week's average to predict tomorrow's temperature (paragraph (50)).
• Customer-support response-time prediction based on static estimation of mean resolution time (paragraph (51)).
• Demand forecasting that predicts average sales per product per day (paragraph (51)).

The Commission's reasoning is that these are baseline or benchmark systems — they predict averages or means rather than achieving the performance of more complex time-series or learnt models.

Important

The Commission cautions against mechanical application of the definition and exclusions (paragraph (6), paragraph (40)). The assessment remains specific to the system's architecture and functionality (paragraph (61)), and no automatic list of systems inside or outside the definition is possible (paragraph (62)). Relabelling a complex inference system as a simple prediction tool should not avoid Article 3(1), but a high-stakes intended purpose alone does not replace the Article 3(1) analysis.

Relationship to the GPAI model definition (Article 3(63))

The AI-system definition (Article 3(1)) and the general-purpose AI model definition (Article 3(63)) are distinct.

• An AI system is the whole computational arrangement that infers outputs and influences environments.
• A general-purpose AI model is a model as such — typically trained with self-supervision on large data, displays significant generality, and is capable of performing a wide range of distinct tasks.

A GPAI model becomes part of an AI system when it is integrated into a system with an intended purpose. The Article 3(1) definition applies to the resulting system. The Chapter V (Articles 51–56) GPAI-model regime applies separately, at model level, to the model provider. See General-purpose AI models.

Interaction with downstream classification

The Article 3(1) definition is the entry test for the AI Act. Once a system is in scope, the four obligation regimes (Article 5 prohibited; Article 6 + Annex I/III high-risk; Article 50 transparency; Chapter V GPAI) determine which obligations apply.

• Article 5 prohibited practices — apply to AI systems regardless of risk classification. See Commission guidance on prohibited practices.
• Article 6 high-risk classification — applies the Annex I product-safety route, the Annex III use-case route, and the Article 6(3) derogation. See Commission draft guidance on high-risk classification.
• Article 50 transparency duties — apply to specific deployments (chatbots, emotion recognition, biometric categorisation, deepfakes, AI-generated content) independently of high-risk status. See Prohibited practices and transparency.
• Chapter V GPAI — applies at model level to GPAI providers.

Settled vs vague (per Commission guidance)

The Commission's guidance settles several questions that were contested before adoption:

Settled:

• Inference is the load-bearing element (paragraph (26)+). Without inference there is no AI system, no matter what other elements are present.
• Adaptiveness is optional (paragraph (23)). Frozen-weight systems are not excluded merely because they do not adapt after deployment, if the other Article 3(1) elements are met.
• Both content generation and decision recommendation are first-class outputs (paragraph (52)+). Generative systems are squarely within the definition.
• 'Machine' includes both hardware and software components (paragraph (11)). The carve-out for purely manual processes is narrow.
• Mathematical-optimisation systems that accelerate established methods without transcending basic data processing are excluded (paragraphs (42)–(45)).
• Basic data-processing systems (database queries, spreadsheets, statistical visualisations) are excluded (paragraphs (46)–(47)).

Left vague:

• The boundary between 'transcends basic data processing' and 'does not transcend basic data processing' is the central judgement call (paragraph (42)). The Commission gives examples but no quantitative threshold.
• The boundary between a fine-tuned GPAI model and a new AI system in its own right is not directly addressed in this guideline.
• How an excluded-category technique (e.g. a mean-based estimator) should be assessed when embedded in a high-stakes workflow is not expressly resolved; the Commission still requires a system-specific Article 3(1) assessment based on architecture and functionality (paragraphs (61)–(62)).
• The status of physics-informed neural networks, scientific-computing approximators and other ML-as-numerical-solver applications beyond the specific Commission examples (paragraphs (43)–(44)) is unsettled.
• The intersection between the AI-system definition and product-safety carve-outs in Article 2 (e.g. military, scientific R&D) is outside the scope of this guideline.

For any unsettled question, document the assessment, the reasoning, and the Commission's closest example or reasoning. Market surveillance authorities and (eventually) the CJEU will read these reasoning trails.

How to operationalise the AI-system definition in Modulos

Modulos models the Article 3(1) scoping question through the framework template MFF-1 and its principal classification requirement MRF-38.

Article / Annex	Commission guidance section	Modulos requirement	Code
Article 3(1)	sections II.1–sections II.7 (seven elements) + sections III.5.2 (carve-outs)	AI System Classification (app)	MRF-38
Article 4 (AI literacy)	Background reading; the definition determines whether Article 4 applies	AI Literacy (org)	ORF-36
Articles 5 / 6 / 50 / 51 (downstream classification)	Outside this guideline; see other commission-guidance pages	AI System Classification (app); Prohibited AI Practices (app); AI System Classification Exemption (app)	MRF-38; MRF-119; MRF-111

The scoping assessment under MRF-38 records the seven-element analysis: a documented determination, per system, of whether each element is present and how the Commission's carve-outs apply. The assessment is owner-authored documentation linked to MRF-38 evidence. Module rationale: keep the legal reasoning trail attached to the requirement so a market surveillance authority can read it directly.

For full template details see Operationalizing the EU AI Act in Modulos.

Cross-framework mapping (preview)

AI-system definition	Adjacent reading
Seven-element framework (paragraph (9))	OECD updated AI-system definition (2024) — the Commission's framework is intentionally close.
Lifecycle perspective (paragraph (10))	ISO/IEC 22989 AI lifecycle stages; NIST AI RMF MAP 2.1 (system characterisation).
Inference as load-bearing element (paragraph (26)+)	NIST AI RMF Section 2 (Characteristics of AI systems).
Mathematical-optimisation carve-out (paragraphs (42)–(45))	ISO/IEC 22989 distinction between AI techniques and traditional optimisation.
Basic-data-processing carve-out (paragraphs (46)–(47))	Not explicitly addressed in NIST or ISO; Commission view is novel.

Related pages

Commission guidance overview

Hub: legal status of Commission guidance, the source documents, how to use them

Prohibited practices (Commission guidance)

Article 5(1)(a)–(h) interpretation + RBI authorisation regime

High-risk classification (Commission guidance)

Article 6(1) / 6(2) / 6(3) decision framework — draft guidance

EU AI Act overview

The underlying Regulation: four obligation regimes, phased application

General-purpose AI models

Chapter V (Articles 51–56) — the GPAI model regime, distinct from the AI-system definition

Operationalizing in Modulos

OFF-1 + MFF-1; the scoping questionnaire that records the Article 3(1) analysis

Source attribution

Commission Communication C(2025) 5053 final, Brussels, 29 July 2025 — Commission Guidelines on the definition of an artificial intelligence system established by Regulation (EU) 2024/1689 (AI Act). © European Union. Adopted under Article 96(1)(f) AI Act. PDF available via the AI Act Service Desk. Paragraph references on this page (e.g. paragraph (9), paragraph (23)) are to the paragraphs of that Communication. The underlying Regulation (EU) 2024/1689 is published in the OJ L of 12 July 2024 under CELEX 32024R1689.

Disclaimer

This page is for general informational purposes and does not constitute legal advice.