DOM Pruning and Semantic Intent Extraction within V8 Isolates

01. ABSTRACT

This paper outlines the technical architecture behind AGENSTAB’s approach to structural simplification: stripping visual noise, executing JavaScript within sterile V8 Isolates, and mapping raw DOM nodes to a Semantic Intent Tree (SIT). Benchmarks indicate that this architecture can reduce LLM context consumption by up to 98% while providing a near-deterministic environment for autonomous agents.

02. ARCHITECTURAL FOUNDATIONS

To execute modern web applications, JavaScript evaluation is mandatory. However, full browser instances are vulnerable to behavioral fingerprinting and significant memory bloat. AGENSTAB utilizes sterile V8 Isolates—independent JavaScript execution environments that do not share memory heaps.

By controlling the V8 runtime at a low level, the engine ensures a consistent hardware and software fingerprint, providing a stable environment for autonomous agents across diverse infrastructure regions.

03. SEMANTIC INTENT MAPPING

The final phase converts the pruned DOM into a JSON-based Semantic Intent Tree (SIT). By converting raw HTML into a dense semantic node, the SIT minimizes the data footprint and allows the intelligence layer to process complex workflows within a single LLM context window.

<div class="p-4 bg-gray-100" id="btn-99">
  <button class="bg-blue-600 text-white">
    Confirm Order
  </button>
</div>

{
  "role": "order_btn",
  "intent": "confirm_checkout",
  "id": "0x4A2"
}

04. CONCLUSION

By intercepting the rendering pipeline at the V8 level and applying algorithmic pruning, autonomous agents can operate at efficiencies previously unattainable. This architecture establishes the framework for institutional-grade web orchestration.