Enabling Agentic AI across the Cloud-Edge Computing Continuum: A Research Roadmap
TL;DR
A continuum-native agent paradigm is argued for that decomposes agents into intelligence, persona, and memory, allowing their independent placement, migration, and replication across the computing continuum, from cloud to edge, IoT, and emerging hardware platforms.
Abstract
Agentic AI is shifting AI applications from passive model inference to goal-driven, tool-using, and collaborative autonomous systems. Yet, current deployments remain concentrated in data centers or powerful personal devices. This paper provides a research roadmap for enabling large-scale, enterprise-oriented agentic AI across the computing continuum, from cloud to edge, IoT, and emerging hardware platforms, where heterogeneity, mobility, energy constraints, and governance requirements fundamentally reshape agent design and operation. We argue for a continuum-native agent paradigm that decomposes agents into intelligence, persona, and memory, allowing their independent placement, migration, and replication. Building on this abstraction, we structure the open research space into three pillars (agent operation, agent connectivity, and agent trustworthiness) and elaborate on potential research directions and challenges. Finally, we outline evaluation directions grounded in representative industrial use cases and benchmarks. The roadmap aims to guide research and development toward enabling the deployment of agentic AI as first-class workloads on the cloud-edge continuum.
