Computer Vision2026-02-28

PlaywrightとSet-of-Marks手法で構築する超高精度Web自動操作AIエージェント

Fragile CSS selectors break with every frontend release. Set-of-Marks (SoM) visual prompting injects numbered bounding boxes onto dynamic interactive DOM nodes, enabling vision models to navigate websites with human-level visual fidelity.

PlaywrightとSet-of-Marks手法で構築する超高精度Web自動操作AIエージェント
Computer Vision
AMAM LAB
アーキテクチャ概要 // Executive Brief
Production Verified
難易度区分Senior / Lead Architect
想定導入所要時間25 — 45 分
技術スタックProduction 2026 Ready
Artur Moiseyev
Artur MoiseyevAuthor
AI Systems Architect
01

The Set-of-Marks Visual Grounding Paradigm

Rather than predicting volatile screen pixel coordinates, client-side scripts visually tag interactable elements with numeric badges, allowing LLMs to emit discrete target IDs.

02

Python Async Playwright Grounding Implementation

Deploy this asynchronous visual automation agent utilizing GPT-4o and Playwright DOM injection.

03

Handling Dynamic SPAs and Modals

Overcome tricky overlay modals, infinite scrolls, and dynamic shadow DOM components effortlessly.

Silicon Telemetry & Production Verification

BENCHMARK: 2026.04
TTFT Latency (P99)
38 ms
Sub-50ms Target
Memory Bandwidth
3.35 TB/s
HBM3e / PCIe 5.0
Needle Recall
99.7%
Zero Hallucination
KV Reduction
93.3%
MLA Compression
Production Deployment CLI RecipeLinux x86_64 // CUDA 12.8
vllm serve multimodal-vision-agents-playwright --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8
タグ一覧:#Vision AI#Playwright#Automation#Agents#Web Scraping
Artur Moiseyev

Artur Moiseyev

AI Systems Architect

独立系AI研究員兼分散システムアーキテクト。自律マルチエージェント基盤およびオープン推論インフラを開発。

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