AI

Application Interface

Interface →
Introduced in R99 Also in: Radio Access Network, Management

AI is a prefix used in 3GPP specifications to denote an Application Interface, specifically a class method that standardizes application-level interface naming for network functions and services.

Category
Interface
Introduced
R99
Where
Services › Codecs
Also touches
2 segments
Specifications
30 specs
AI Description Purpose Related Classification Specifications

Description

In 3GPP specifications, the term 'AI' serves as a standardized prefix for Application Interface class methods. It is a naming convention used within interface definitions to clearly identify methods that belong to the application layer of network functions. This prefix is applied across various technical specifications (TS) to maintain consistency in how application programming interfaces (APIs) and service-based interfaces (SBIs) are documented and implemented.

The AI prefix is typically found in the context of Open Service Architecture (OSA) and later in the Service-Based Architecture (SBA) of the 5G Core network. It precedes the actual method name, forming a complete identifier like 'AI_<MethodName>'. This structured naming helps in distinguishing application-layer operations from transport, session, or management-layer functions within complex network element interfaces. The methods themselves define the operations that an application can invoke on a network function or that network functions can expose to each other, such as service registration, discovery, invocation, and policy management.

Architecturally, interfaces using the AI prefix are part of the broader framework for enabling third-party application interaction with network capabilities, as defined in specifications like TS 23.090 (Open Service Access) and TS 23.271 (Location Services). In modern 5G systems, this concept evolves into the Network Exposure Function (NEF) and standardized APIs, but the AI prefix historically provided a clear marker for application-relevant methods within interface specifications. Its usage ensures that during system design, code generation, and testing, engineers can easily identify and handle methods intended for application-level logic and service exposure.

Purpose & Motivation

The AI prefix was introduced to solve the problem of inconsistent and ambiguous naming for application-layer methods within 3GPP interface specifications. Prior to its standardization, different working groups and releases might use varied naming conventions (like 'App', 'Srv', or no prefix at all) for similar interface methods, leading to confusion during implementation and integration between network equipment from different vendors. The prefix creates a uniform, easily recognizable pattern that denotes a method's belonging to the application interface domain.

Its creation was motivated by the need for clear, maintainable, and interoperable specifications as 3GPP networks began to expose more capabilities to external applications, starting with initiatives like the Open Service Access (OSA) and Parlay/OSA APIs. By tagging these methods with 'AI', the standards body ensured that anyone reading a technical specification could immediately understand the functional layer of the operation, streamlining the development of network elements and client applications that rely on these standardized interfaces.

Classification

Part ofAPI
Related approachesOSANEF

Evolution Across Releases

R99 Initial

Introduced as the prefix 'AI' for Application Interface class methods within 3GPP specifications, primarily in the context of Open Service Access (OSA) and CAMEL service environment. It established a consistent naming convention to identify application-layer operations in interface definitions, aiding in the standardization of how network capabilities are exposed to applications.

Explore further

Broader topics and technologies where AI plays a role.

Defining Specifications

3GPP specifications that define or reference AI, with the latest known release. Sourced from the 3GPP document catalog — see methodology.

SpecificationTitleRelease
TR 21.905 vj00 3GPP Technical Terms and Definitions Rel-19
TS 22.156 vj10 Mobile Metaverse Services Rel-19
TR 22.829 vh10 Enhancement for UAVs; Stage 1 Rel-17
TR 22.856 vj20 Feasibility Study on Localized Mobile Metaverse Services Rel-19
TR 22.873 vi00 Technical Report on IMS Multimedia Telephony Service Enhancements Rel-18
TR 22.874 vi20 Technical Report Rel-18
TR 22.890 vj00 Study on Railway Smart Station Services Rel-19
TS 23.090 vj00 USSD Stage 2 Specification Rel-19
TS 23.171 v1300 LCS Stage 2 Specification for UMTS Rel-4
TS 23.271 vj00 LCS Stage 2 Specification Rel-19
TS 23.700 vk00 XR Services Application Enablement Layer Rel-20
TS 25.211 vj00 UTRA FDD Layer 1: Transport & Physical Channels Rel-19
TS 26.847 vj00 AI/ML Evaluation in 5G Media Services Rel-19
TS 26.854 vj00 Study on Haptics in 5G Media Services Rel-19
TR 26.927 vj00 AI/ML in 5G Media Services Study Rel-19
TR 26.928 vj00 Study on eXtended Reality (XR) in 5G Rel-19
TR 26.956 vj01 Beyond 2D Video Formats & Codecs Study Rel-19
TS 28.104 vj30 Management Data Analytics (MDA) Rel-19
TS 28.105 vj30 AI/ML Management for 5GS Rel-19
TR 28.809 vh00 Enhancement of Management Data Analytics (MDA) Study Rel-17
TS 33.784 vj00 Security aspects of AI/ML in core network Rel-19
TR 33.877 vi00 Technical Report on Security Aspects of AI/ML in RAN Rel-18
TR 33.898 vi01 Technical Report on 5GS AI/ML Security Rel-18
TS 37.340 vj00 Multi-Connectivity Operation Overview Rel-19
TS 37.355 vj20 LTE Positioning Protocol (LPP) Rel-19
TS 38.300 vj00 NG-RAN Overall Description Rel-19
TS 38.305 vj00 NG-RAN UE Positioning Stage 2 Rel-19
TS 38.401 vj10 NG-RAN Architecture Specification Rel-19
TS 38.423 vj10 Xn Application Protocol (XnAP) specification Rel-19
TS 38.843 vj00 Study on AI/ML for NR Air Interface Rel-19
Patrick Zandl

About the author: Patrick Zandl (b. 1974)

Telecommunications specialist, technology journalist (founder of the Mobil server), and developer who has been running since 2025 — the largest Czech-language resource on AI-assisted programming. Formerly Chief Wizard Architect at Prusa3D and head of development for Turris at CZ.NIC; currently a consultant and instructor on AI implementation in companies.