# Intent Assurance using LLMs guided by Intent Drift

arXiv: https://arxiv.org/abs/2402.00715 · HTML: https://mlsanity.com/paper/2402.00715/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [LLM-based policy generation for intent-based management of applications](https://mlsanity.com/paper/2402.10067/) (2024)
- [End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study](https://mlsanity.com/paper/2201.13300/) (2022)
- [Multi-agent reinforcement learning for intent-based service assurance in cellular networks](https://mlsanity.com/paper/2208.03740/) (2022)

## Built on by (most cited first, 13 of 13)

- [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025)
- [Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration](https://mlsanity.com/paper/2404.15869/) (2024)
- [Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems](https://mlsanity.com/paper/2510.12462/) (2025)
- [LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score](https://mlsanity.com/paper/2602.13672/) (2026)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [Large Language Model Data Generation for Enhanced Intent Recognition in German Speech](https://mlsanity.com/paper/2508.06277/) (2025)
- [Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things](https://mlsanity.com/paper/2512.20627/) (2025)
- [Intent2QoS: Language Model-Driven Automation of Traffic Shaping Configurations](https://mlsanity.com/paper/2601.18974/) (2026)
- [AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks](https://mlsanity.com/paper/2603.23772/) (2026)
- [IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations](https://mlsanity.com/paper/2608.02110/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [Agentic Pressure: The Endogenous Entropy of Reliable Autonomy](https://mlsanity.com/paper/2609.05995/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Intent-Based Networking (IBN) presents a paradigm shift for network management, by promising to align intents and business objectives with network operations--in an automated manner. However, its practical realization is challenging: 1) processing intents, i.e., translate, decompose and identify the logic to fulfill the intent, and 2) intent conformance, that is, considering dynamic networks, the logic should be adequately adapted to assure intents. To address the latter, intent assurance is tasked with continuous verification and validation, including taking the necessary actions to align the operational and target states. In this paper, we define an assurance framework that allows us to detect and act when intent drift occurs. To do so, we leverage AI-driven policies, generated by Large Language Models (LLMs) which can quickly learn the necessary in-context requirements, and assist with the fulfillment and assurance of intents.