  Agents centered around Large Language Models (LLMs) are now capable of
automating mobile device operations for users. After fine-tuning to learn a
user's mobile operations, these agents can adhere to high-level user
instructions online. They execute tasks such as goal decomposition, sequencing
of sub-goals, and interactive environmental exploration, until the final
objective is achieved. However, privacy concerns related to personalized user
data arise during mobile operations, requiring user confirmation. Moreover,
users' real-world operations are exploratory, with action data being complex
and redundant, posing challenges for agent learning. To address these issues,
in our practical application, we have designed interactive tasks between agents
and humans to identify sensitive information and align with personalized user
needs. Additionally, we integrated Standard Operating Procedure (SOP)
information within the model's in-context learning to enhance the agent's
comprehension of complex task execution. Our approach is evaluated on the new
device control benchmark AitW, which encompasses 30K unique instructions across
multi-step tasks, including application operation, web searching, and web
shopping. Experimental results show that the SOP-based agent achieves
state-of-the-art performance in LLMs without incurring additional inference
costs, boasting an overall action success rate of 66.92\%. The code and data
examples are available at https://github.com/alipay/mobile-agent.
