Intent-Based Agentic AI Framework For Data Placement in Compute Continuum

Modern data-intensive applications require adaptive, goal-driven infrastructures, yet the growing complexity of the compute continuum makes it increasingly difficult for existing approaches to autonomously manage data placement in accordance with evolving user intents and real-time system states. In this paper, we propose a modular Intent-Based Agentic AI Framework that can accurately capture high-level user objectives, reason over distributed resource conditions, and automate data placement in a scalable and interpretable manner. Unlike static rule-based systems, our framework supports interactive user-agent interactions and adaptive forecasting to recommend and apply optimal storage and caching strategies across distributed cloud-edge environments. Our prototype implementation showed an accurate response to user inputs at 88%, and achieved objectives in around 30 seconds. This implementation and results demonstrate the feasibility and efficacy of the proposed solution.