AppAgent-Pro: Proactive GUI Agent

• AppAgent-Pro is an advanced proactive GUI agent that integrates multimodal data across applications using a three-stage pipeline.
• It leverages large language models and dynamic sub-task generation to anticipate user needs and orchestrate efficient multi-app workflows.
• Evaluations on benchmarks like MobileAgentBench and DroidTask demonstrate its high adaptability, personalized user assistance, and potential for continuous improvement.

AppAgent-Pro is an advanced proactive graphical user interface (GUI) agent system designed to autonomously integrate information across multiple application domains, anticipate user needs, and orchestrate efficient information acquisition workflows. Leveraging LLMs and multimodal data parsing, AppAgent-Pro transcends the limitations of reactive agents by enabling dynamic, context-aware interactions with mobile devices. Its architecture, pipeline, evaluation strategies, and impact on user assistance and information mining are described in detail below.

1. System Architecture and Operational Pipeline

AppAgent-Pro is structured as a three-stage pipeline: Comprehension, Execution, and Integration (Zhao et al., 26 Aug 2025). The system’s architecture is depicted in workflow diagrams, illustrating how user queries are processed and information is retrieved and amalgamated across multiple apps.

• Comprehension: The agent utilizes a LLM (e.g., GPT-4) to parse and analyze user queries, extracting both explicit instructions and latent needs. This semantic understanding forms the foundation for subsequent planning steps.
• Execution: The agent operates in two distinct modes:
  • Shallow Execution Mode: Executes direct search queries on relevant applications and gathers high-level results, such as page titles or simple textual snippets.
  • Deep Execution Mode: Unfolds a recursive and dynamic sub-query generation process, dispatching tailored queries across a constellation of apps (such as YouTube, Amazon), inspecting multi-level results, and iteratively refining the output set.
• Integration: The agent synthesizes textual results, visual content (such as screenshots or video thumbnails), and context from historical interaction logs to generate an aggregated, multimodal response.

The pipeline enables transition from purely reactive paradigms (where agents only respond to direct commands) to proactive ones, anticipating user needs and assembling holistic reply packages.

2. Proactive Information Integration and Multidomain Orchestration

A distinguishing feature of AppAgent-Pro is its capacity for multidomain information mining using proactive inference (Zhao et al., 26 Aug 2025). When presented with a query, the system employs LLM-based semantic parsing to:

• Identify implicit requirements and information gaps.
• Formulate multiple sub-tasks targeting specific external apps; for example, it might concurrently search for "cat care tips" videos on YouTube and "essential cat supplies" on Amazon given the query "how to keep a cat".
• Iteratively expand and optimize the sub-query strategy by using continuous feedback derived from retrieved data and user history.

The process can be formalized as

$S^* = \arg\max_s F(Q, s, C)$

where $Q$ is the original query, $s$ denotes a sub-task derived from $Q$, and $C$ represents accumulated contextual information. This optimization enables comprehensive, context-sensitive information synthesis.

3. Action Space, Exploration, and Deployment Mechanisms

Underlying AppAgent-Pro’s high task completion rates are innovations around action space construction, multimodal exploration, and deployment strategies (Li et al., 5 Aug 2024):

• Flexible Action Space: The agent integrates parser-based and vision-based approaches, supporting commands such as TapButton, Text, LongPress, Swipe, Back, Home, Wait, and Stop. Commands can target UI elements via numerical IDs (from XML parsers) or via OCR-extracted text, greatly increasing adaptability to non-standard interfaces.
• Exploration Phase: The agent, either autonomously (agent-driven) or in tandem with human operators (manual exploration), documents UI element functions through screenshot comparisons, action logging, and semantic memory encoding. A reflection module filters ineffective or irrelevant actions, updating a "useless_list" that informs future decision-making.
• Deployment Phase: Retrieval-Augmented Generation (RAG) dynamically integrates current GUI state with knowledge base embeddings via a self-query retriever, updating prompts during execution and maintaining memory of previous actions for high-precision, multi-step workflows.

4. Evaluation Methodology and Task Performance

AppAgent-Pro’s performance has been assessed across diverse benchmarks, including MobileAgentBench (Wang et al., 12 Jun 2024), DroidTask, AppAgent, and Mobile-Eval (Li et al., 5 Aug 2024, Zhao et al., 26 Aug 2025):

Benchmark	Metric	AppAgent-Pro Performance
MobileAgentBench	Success Rate (SR)	SR ≈ 0.40 (AppAgent highest among agents)
DroidTask	Completion Rate (CR)	77.8% (Ours), outperforming baselines
AppAgent benchmarks	SR (manual docs)	Up to 93.3%
Mobile-Eval	Relative Efficiency	Near 100% in select categories

Evaluation metrics are rigorously defined in LaTeX notation, e.g.,

• $SR = \frac{N_{\text{success}}}{M_{\text{tasks}}}$
• $SE = \frac{S_{\text{actual}}}{S_{\text{min}}}$
• $FN = \frac{N_{\text{early}}}{M_{\text{failure}}}$
• $FP = \frac{N_{\text{late}}}{M_{\text{success}}}$

MobileAgentBench emphasizes verification of task success using the final UI state and key app events, as opposed to strict alignment with canonical action sequences. This approach allows agents like AppAgent-Pro to recover from intermediate errors or employ exploratory strategies without penalty, though it can introduce elevated false positive termination rates ($FP \approx 0.40$ for AppAgent).

5. User Assistance Features and Societal Impact

AppAgent-Pro offers distinct enhancements in user support compared to reactive agents (Zhao et al., 26 Aug 2025):

• Intelligent Anticipation: The agent predicts latent needs and initiates cross-app information retrieval without explicit user instructions, reducing the need for user intervention.
• Rich Multimodal Output: Responses blend structured text with screenshots and other visual elements, improving result interpretability.
• Personalization: Integration of historical user interaction data enables tailored replies and minimization of redundant searches, thus lowering cognitive burden.
• Societal Impact: The agent's proactive orchestration of information across apps and modalities may democratize complex information access and support broader, deeper human-information interaction paradigms.

6. Technical Implementation and Practical Use Cases

AppAgent-Pro is implemented in Python and employs state-of-the-art LLMs, RAG pipelines, and multimodal perception modules (Zhao et al., 26 Aug 2025, Li et al., 5 Aug 2024). The demonstration web interface is built using Streamlit.

• Modular Design: Segregating comprehension, execution, and integration modules facilitates scalability and updateability.
• Real-World Scenarios: Example queries such as "How to upload a video on YouTube?" trigger targeted searches, result aggregation, and visual annotation. More complex queries induce multi-app task orchestration.
• Safety Mechanisms: For tasks involving sensitive operations (e.g., password entry, payments), the agent invokes manual override for user safety.

7. Limitations and Future Research Directions

Multiple avenues for further research are highlighted (Zhao et al., 26 Aug 2025, Li et al., 5 Aug 2024):

• Extending Application Coverage: Incorporation of additional apps and data sources would increase domain generality.
• Advancing Intent Inference: More sophisticated algorithms for predicting latent needs will improve proactive sub-task generation.
• Human–AI Co-adaptation: Dynamic, real-time adjustment in response to evolving user preferences warrants exploration.
• Robustness to UI Variability: Addressing issues such as hidden UI elements and embedded tags requires continued advancement in multimodal perception.
• Continuous Learning Mechanisms: Adapting to rapidly evolving app environments remains an open challenge for robustness and scalability.

AppAgent-Pro thus represents an integrative, context-aware evolution in the domain of LLM-based GUI agents, combining multimodal exploration, proactive orchestration, and rigorous evaluation to advance the state of intelligent mobile interaction systems.