DOM Pruning and Semantic Intent Extraction within Chromium sandboxes

01. ABSTRACT

This paper outlines the technical architecture behind AGENSTAB’s approach to structural simplification: stripping visual noise, executing JavaScript within sterile Chromium sandboxes, 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 Chromium sandboxes—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. METHODOLOGY

Our empirical approach involves sampling 50,000 top enterprise portals, extracting their raw accessibility trees, and applying a series of algorithmic pruning techniques. We utilized a cluster of 500 isolated V8 instances to simulate autonomous agent traversals, collecting telemetry on execution latency, token density, and deterministic action success rates.

We measured baseline performance using standard Playwright scripts and compared the delta against AGENSTAB's proprietary extraction protocol. The primary metric of success was the ratio of semantic intent nodes retained versus raw DOM nodes discarded without breaking interactive workflows.

04. 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"
}

05. BENCHMARK DATA

Our internal load tests demonstrated a reduction in context window consumption and latency when utilizing the SIT over raw DOM inputs. The table below highlights the performance deltas observed across 10,000 procurement workflows on a controlled SAP Fiori staging instance. Note: this compares scripted automation (Playwright selectors) against semantic automation (SIT primitives) — not competing AI agent frameworks.

* Internal benchmark results from a controlled staging environment. Not independently verified by a third party. Success rate measures action completion on a fixed set of SAP Fiori procurement forms, not general web navigation. Results may not generalize to all web applications.

06. 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.

07. METHODOLOGY NOTE

This document is an internal technical overview published by the AGENSTAB engineering team, not a peer-reviewed academic paper. The benchmark data presented in Section 05 was collected from controlled internal testing environments and has not been independently verified by a third party. Detailed methodology, test harness configuration, and raw data are available to enterprise customers and prospective partners under NDA upon request.

Contact: research@agenstab.com