Intent-Driven 6G Service Orchestration: Grounded Translation, Validation, and Decomposition

Jean Martins, Leonid Mokrushin, Marin Orlic, Amardeep Kumar A

Keywords: Intent-Based Networking, Agentic AI, LLM, 6G, Constraint Satisfaction, SHACL, Semantic Catalog

TL;DR: An agentic LLM pipeline that grounds intent generation in a semantic service catalog, validates structure via SHACL, and decomposes intents into feasible service orders through constraint satisfaction over QoS capability envelopes.

Abstract: Intent-based automation for 6G envisions networks steered by high-level goals rather than low-level configurations. Existing LLM-based approaches translate natural language into plausible intent representations but typically omit what production deployment requires: grounding in actual service catalogs, formal validation, and cross-layer decomposition. We address this with an agentic workflow comprising three coupled reasoning layers: (i) grounding the translation in a semantic service catalog that exposes TMF-compliant service specifications; (ii) validation of the RDF intent via SHACL structural checking against the TMF Intent Ontology; and (iii) decomposition that selects a CFSS profile via constraint satisfaction over QoS capability envelopes, then covers its infrastructure requirements with RFSS profiles via weighted set cover. Across 930 benchmark runs over six GPT-4.1/5 models, the workflow achieves 97% success in structured mode and 90% on average across natural-language scenarios, with 100% correct rejection of infeasible requests. Grounding LLM context in catalog capability metadata reduces adversarial hallucinations by 26 percentage points; larger gains than scaling model size alone.

Submission Number: 31