Local AI on the Move

Despite all of the incredible advances we have seen in the world of generative artificial intelligence (AI) over the past few years, we have to keep things in perspective — we are still at the Yahoo and Netscape stage of the technology. Many changes are sure to come that will soon make our present large language models and image generators look quaint by comparison. Among these changes, some of the most widely anticipated for the near future are enhanced personalization and privacy.

To bring about these advances, AI models will have to move out of the cloud and into edge computing systems. The release of more powerful hardware, combined with algorithmic optimizations, is rapidly making this scenario a possibility. Ever more powerful AI models are becoming capable of running on modest edge hardware. And these local models allow for personalization and privacy that a one-size-fits-all cloud model never could.

But as a team led by researchers at the University of Oulu in Finland points out, this trend will lead to a new problem. Edge computing systems are only accessible in a relatively small geographical area. However, we need to access these systems via mobile devices as we move about during the day. That means we will have to wirelessly jump from one AI agent to the next without missing a beat.

Yet existing wireless technologies only hand off the network connection itself between access points. The researchers recognized that a lot more context will also have to be passed along for the new agents to pick up where the previous ones left off. For this reason, they have introduced what they call the Wireless AI Agent Network (WAAN). WAAN was designed to transfer the execution context of user intents across AI agents as users move.

In practice, WAAN takes the idea of a handover — the switching process familiar from cellular networks — and expands it beyond connectivity. Instead of just keeping a signal alive, it ensures that whatever task your AI was working on continues seamlessly. For example, if a personal agent on your phone is in the middle of interpreting your request for restaurant recommendations, WAAN makes sure the new edge agent you connect to receives not only your data but also the state of the ongoing task. Without this continuity, you would face delays, redundant computation, and wasted energy every time you moved.

To accomplish this, WAAN introduces a cross-layer framework. Unlike most agentic systems that operate only at the application level, WAAN integrates awareness from the wireless channel all the way up to the user’s intent. TinyML agents embedded in even resource-constrained devices play an important role. These lightweight models continuously monitor factors like signal strength, CPU load, or battery life, and they negotiate with neighboring agents to decide whether to offload a task, keep it local, or hand it over.

The system also features what are called “semi-stable rendezvous points.” These are fixed coordination anchors where contextual information can be cached. If a mobile device moves mid-task, the rendezvous point preserves the execution state so that another agent can pick up where the last one left off.

As AI models become more personal and private, and emerging wireless standards such as 6G provide more bandwidth to support their local deployments, WAAN could prove to be an essential piece of the puzzle. Maintaining connectivity and context between agents is no small task, but it is a problem that must be solved to maintain forward progress in the field.