  Since the advent of personal computing devices, intelligent personal
assistants (IPAs) have been one of the key technologies that researchers and
engineers have focused on, aiming to help users efficiently obtain information
and execute tasks, and provide users with more intelligent, convenient, and
rich interaction experiences. With the development of smartphones and IoT,
computing and sensing devices have become ubiquitous, greatly expanding the
boundaries of IPAs. However, due to the lack of capabilities such as user
intent understanding, task planning, tool using, and personal data management
etc., existing IPAs still have limited practicality and scalability. Recently,
the emergence of foundation models, represented by large language models
(LLMs), brings new opportunities for the development of IPAs. With the powerful
semantic understanding and reasoning capabilities, LLM can enable intelligent
agents to solve complex problems autonomously. In this paper, we focus on
Personal LLM Agents, which are LLM-based agents that are deeply integrated with
personal data and personal devices and used for personal assistance. We
envision that Personal LLM Agents will become a major software paradigm for
end-users in the upcoming era. To realize this vision, we take the first step
to discuss several important questions about Personal LLM Agents, including
their architecture, capability, efficiency and security. We start by
summarizing the key components and design choices in the architecture of
Personal LLM Agents, followed by an in-depth analysis of the opinions collected
from domain experts. Next, we discuss several key challenges to achieve
intelligent, efficient and secure Personal LLM Agents, followed by a
comprehensive survey of representative solutions to address these challenges.
