AppAgent: Multimodal Agents as Smartphone Users
This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing on 50 tasks across 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.
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Handle multi-step workflows autonomously
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Schedule meeting → Find time → Send invite → Confirm attendees
Save 5-10 hours/week on routine coordination tasks
Gather data from multiple sources and summarize
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Research competitor pricing across 5 websites, create comparison table
Reduce research time from hours to minutes
Analyze options and recommend actions
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Review 20 vendor proposals, score against criteria, rank top 3
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AI agents combine large language models with tools, memory, and decision-making logic to autonomously complete multi-step tasks without constant human guidance.
Large language model for reasoning and decision-making
Understand tasks, plan steps, generate responses
APIs, databases, external services the agent can call
Take actions beyond text generation (search, compute, write files)
Short-term (conversation) and long-term (persistent) memory
Maintain context across interactions and learn from past actions
Decision engine for choosing next action
Plan multi-step workflows and handle errors/edge cases
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I recommend Tencent for teams already running multiple AI agents; the listing helped us narrow the short list quickly.
Tencent reduced evaluation time — saves/upvotes on explainx.ai correlated with fewer surprises in the trial.
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