Enterprise AI Analysis
PG-Agent: An Agent Powered by Page Graph
Traditional GUI agents learn from isolated, linear user actions, failing to grasp the complex web of interactions within enterprise applications. This research introduces PG-Agent, a revolutionary approach that transforms sequential user episodes into a structured "Page Graph." This graph acts as a dynamic map of the application, enabling the agent to make smarter, context-aware decisions, dramatically improving its ability to automate complex tasks and generalize to new scenarios.
Executive Impact Summary
The PG-Agent methodology demonstrates significant, measurable improvements in automation reliability, generalization, and data efficiency. These advancements translate directly into reduced manual effort for QA, faster process automation, and a lower cost of training for enterprise-wide AI agents.
0%
Improvement in Cross-Domain Task Success
0.0%
Relative Performance Lift Over Base Model
0%
Reduction in Training Data Needed
0.0%
Accuracy Gain in Cross-App Navigation
Deep Analysis & Enterprise Applications
Explore the core components of the PG-Agent framework, from the foundational Page Graph concept to its advanced multi-agent workflow and benchmarked performance. Each module below details a key innovation from the research.
The core innovation is an automated pipeline that converts linear user interaction logs into a rich, structured graph. This graph explicitly maps the relationships between UI pages, serving as a powerful knowledge base for the agent.
Enterprise Process Flow
Analyze Action Tuple
→
Page Jump Determination
→
Node Similarity Check
→
Page Graph Update
PG-Agent utilizes a sophisticated multi-agent framework powered by Retrieval-Augmented Generation (RAG). This allows the system to query the Page Graph for relevant navigation paths, decompose complex tasks, and execute actions with high precision.
Enterprise Process Flow
Observation Agent Perceives
→
RAG Retrieves Guidelines
→
Planning Agents Decompose Task
→
Decision Agent Executes
The ability to generalize knowledge is critical for enterprise AI. When tested on completely unseen websites and applications (cross-domain), PG-Agent significantly outperforms previous state-of-the-art models by leveraging its structural understanding of UI navigation.
| Model / Approach | Cross-Domain Task Success Rate (Mind2Web) |
|---|---|
| PG-Agent (This research) |
|
| Previous SOTA (OmniParser) |
|
| Base MLLM (Qwen2.5-VL-72B) |
|
Analysis reveals that the strategic injection of retrieved guidelines is crucial for performance. Furthermore, case studies show the agent's ability to complete complex, multi-step tasks that mimic real-world user behavior.
+5.6%
Performance lift in Cross-Domain Step SR when RAG guidelines are injected into the Sub-Task Planning Agent, demonstrating the value of targeted knowledge injection.
Case Study: Multi-Step Task Automation
Challenge: Automating the installation of a new application involves a long, precise sequence of actions: navigating to an app store, using the search function, identifying the correct application from a list of results, and initiating the install command.
PG-Agent Solution: By querying the Page Graph, PG-Agent retrieves a high-probability action sequence. Its multi-agent framework then decomposes this into sub-goals: (1) Open Play Store, (2) Type "Yahoo" in search, (3) Click the correct app icon, (4) Click "Install".
Enterprise Outcome: The agent successfully navigates the complex UI flow from start to finish. This demonstrates a robust, goal-oriented capability that can be directly applied to automating internal software testing, employee onboarding, or repetitive data entry tasks across multiple applications.
Calculate Your Automation ROI
Estimate the potential annual savings and reclaimed work hours by implementing PG-Agent to automate repetitive GUI-based tasks within your organization. Adjust the sliders based on your team's specific workload.
Potential Annual Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
Deploying PG-Agent is a strategic, phased process designed to maximize value and minimize disruption. We guide you from initial knowledge capture to enterprise-wide, self-improving automation.
Phase 1: Knowledge Ingestion & Graph Construction
We analyze your existing process documentation and record expert users interacting with your key internal applications. This data is used to automatically construct the foundational Page Graphs for your core business processes.
Phase 2: Agent Integration & Pilot Program
The PG-Agent is integrated into a controlled environment to automate a high-value, well-defined task (e.g., software testing, report generation). This pilot program validates performance and measures initial ROI.
Phase 3: Workflow Customization & Expansion
We customize the multi-agent framework to align with your specific business logic, compliance rules, and exception handling protocols. The agent's capabilities are then expanded to adjacent tasks and departments.
Phase 4: Enterprise Rollout & Continuous Learning
The solution is rolled out across the enterprise. A feedback loop is established to allow the Page Graphs to be continuously updated with new interactions, ensuring the agent adapts to software updates and evolving business processes.
Unlock a New Era of Automation
Move beyond brittle, script-based automation. PG-Agent offers a robust, intelligent solution that understands your applications like a human expert. Schedule a consultation to discuss how this technology can be tailored to your enterprise needs.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat