Agentization of Digital Assets for the Agentic Web: Concepts, Techniques, and Benchmark

Abstract

Agentic Web, as a new paradigm that redefines the internet through autonomous, goal-driven interactions, plays an important role in group intelligence. As the foundational semantic primitives of the Agentic Web, digital assets encapsulate interactive web elements into agents, which expand the capacities and coverage of agents in agentic web. The lack of automated methodologies for agent generation limits the wider usage of digital assets and the advancement of the Agentic Web. In this paper, we first formalize these challenges by strictly defining the A2A-Agentization process, decomposing it into critical stages and identifying key technical hurdles on top of the A2A protocol. Based on this framework, we develop an Agentization Agent to agentize digital assets for the Agentic Web. To rigorously evaluate this capability, we propose A2A-Agentization Bench, the first benchmark explicitly designed to evaluate agentization quality in terms of fidelity and interoperability. Our experiments demonstrate that our approach effectively activates the functional capabilities of digital assets and enables interoperable A2A multi-agent collaboration. We believe this work will further facilitate scalable and standardized integration of digital assets into the Agentic Web ecosystem.

Figures (5)

• Figure 1: Conceptual illustration of agentization of digital assets for the Agentic Web. Through agentization, digital assets are transformed into A2A-compliant agents that can be integrated into the Agentic Web, enabling interactions and collaborations for real-world tasks.
• Figure 2: Processing pipeline of repository agentization. The repository agentization process starts from raw repository contents and is organized into four stages. The agent first initializes the repository environment, then analyzes repository contents to extract skills, instantiates an inner agent, and finally produces an A2A-compliant agent with an agent card.
• Figure 3: Execution task diversity analysis. Single-repo tasks are distributed across 9 application domains, illustrating broad coverage of domain-specific execution scenarios. Multi-repo tasks are grouped by the number of distinct domains involved in each workflow (cross-$k$ domains), highlighting the prevalence of cross-domain interactions and increasing interoperability requirements.
• Figure 4: Execution task complexity distribution. For single-repo tasks, difficulty is decomposed into several dimensions , measured by corresponding indicators. For multi-repo tasks, difficulty is determined by orchestration complexity, measured by the length of the linear collaboration chain (i.e., the number of sequentially invoked repositories). Tasks are grouped into easy, medium, and hard tiers based on these respective criteria.
• Figure 5: The orchestration mechanism instantiated in our experiments for A2A-Agentization Bench Stage 3. Multi-repo tasks are provided together with their oracle subtask decompositions obtained during benchmark construction, which serve as the input of orchestration process. In parallel, agent cards produced by agentization agent are converted into agent skills. The coordinator relies solely on these skills to select and bind appropriate A2A agents for each subtask, orchestrates their execution and obtains the result.