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Contract-based Agentic Intent Framework for Network Slicing in O-RAN

Fransiscus Asisi Bimo, Chun-Kai Lai, Zhi-Yuan Yang, Ray-Guang Cheng

TL;DR

Experimental results show that the closed-loop agentic pipeline of the proposed CAIF can effectively eliminate harmful intent executions observed in direct-actuation baseline approaches.

Abstract

Intent-based networking aims to simplify network operation by translating operator intents into a collection of policies, configurations, and control actions. However, this translation process relies on heuristics and loose coupling. It often results in unpredictable behavior and ambiguous safety standards. This paper presents a Contract-based Agentic Intent Framework (CAIF) for the radio access network (RAN). The proposed framework employs a closed-loop agentic pipeline that systematically audits user objectives against formal RAN constraints prior to actuation. The proposed CAIF decouples probabilistic intent extraction from strictly governed policy execution to enable the enforcement of deterministic safety guarantees. We use network slicing as a representative use case to demonstrate the design flow and validate the effectiveness of the proposed approach on an O-RAN testbed. Experimental results show that the closed-loop agentic pipeline of the proposed CAIF can effectively eliminate harmful intent executions observed in direct-actuation baseline approaches.

Contract-based Agentic Intent Framework for Network Slicing in O-RAN

TL;DR

Experimental results show that the closed-loop agentic pipeline of the proposed CAIF can effectively eliminate harmful intent executions observed in direct-actuation baseline approaches.

Abstract

Intent-based networking aims to simplify network operation by translating operator intents into a collection of policies, configurations, and control actions. However, this translation process relies on heuristics and loose coupling. It often results in unpredictable behavior and ambiguous safety standards. This paper presents a Contract-based Agentic Intent Framework (CAIF) for the radio access network (RAN). The proposed framework employs a closed-loop agentic pipeline that systematically audits user objectives against formal RAN constraints prior to actuation. The proposed CAIF decouples probabilistic intent extraction from strictly governed policy execution to enable the enforcement of deterministic safety guarantees. We use network slicing as a representative use case to demonstrate the design flow and validate the effectiveness of the proposed approach on an O-RAN testbed. Experimental results show that the closed-loop agentic pipeline of the proposed CAIF can effectively eliminate harmful intent executions observed in direct-actuation baseline approaches.
Paper Structure (16 sections, 8 figures, 5 tables)

This paper contains 16 sections, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Contract-based Agentic Intent Framework
  3. Contract-based Intent Model
  4. Agentic AI for Dynamic Translation
  5. Prompt Techniques
  6. Intent Translation Pipeline
  7. CAIF Implementation on the O-RAN Platform
  8. SLA Slice and A1 Policy Handler
  9. Closed-Loop RAN Slice Control with E2 KPM and RC
  10. Experimental Results
  11. Experimental Setup and Methodology
  12. Semantic Integrity and Output Validation
  13. End-to-End Intent-Based Network in SLA Slice Use Case
  14. Single Intent Assurance Evaluation
  15. Dynamic Intent Assurance Evaluation
  16. ...and 1 more sections

Figures (8)

  • Figure 1: Intent Profiling Prompt
  • Figure 2: System architecture of a CAIF
  • Figure 3: Closed-Loop RAN Slice Control Algorithm
  • Figure 4: Overall Accuracy with 95% Confidence Intervals
  • Figure 5: Field-Level Accuracy with 95% Confidence Interval
  • ...and 3 more figures