Editing Machine Learning at the Edge (section)

==='''Usage and Applications of AI Agents'''===
As artificial intelligence and machine learning technologies continue to mature, they pave the way for the development of intelligent AI agents capable of autonomous, context-aware behavior, with the goal of efficiently performing tasks specified by users. These agents combine perception, reasoning, and decision-making to execute tasks with minimal human intervention. When deployed on edge devices, AI agents can operate with low latency, preserve user privacy, and adapt to local data—making them ideal for real-time, personalized applications in homes, vehicles, factories, and beyond.

To function effectively, an agent must first perceive its environment and understand the task—often defined by the user. Then, it must reason about the optimal steps to accomplish that task, and finally, it must act on those decisions. These three components—perception, reasoning, and action—are essential to the agent’s ability to operate accurately and autonomously in dynamic environments.

'''Reasoning:''' The agent must be able to think sequentially, and decompose its specified tasks into a sequence of specific steps in order to accomplish its goal. It must also have some memory storage in order to remember what it has done, as well as the results of its sequence of actions in order to learn for future steps.

'''Autonomy:''' The agent must choose from the availability of possible steps, and operate based on its reasoning without step-by-step instructions from the user.

'''Tools:''' These tasks, however, are impossible to accomplish without the correct tools. Even if an AI agent understands how to go about carrying a task for optimal results, it must have the actual means to do it. This can include the ability to use and interact with APIs, interpret code, and access certain databases. 

Utilizing AI agents on edge devices can be tricky due to the computational and reasoning power needed. However, there are methods to accomplish this such as SLMs which query LLMs as needed (discussed later), or utilizing more powerful edge devices to carry out tasks. However, utilizing edge devices can be paramount if latency is a major issue, or if the agent is exposed to sensitive user data. Additionally, by using edge devices specific to a user, it may be able to better learn a user's patterns and preferences and react accordingly to provide the best possible outcome for that user.