Autonomous Browsing using Large Action Models
summary
A working autonomous browser agent stacks a Lavage-based multiagent pipeline with a Selenium grid-backed browser, a multimodal supervision engine that reads screenshots, and an interaction engine that retrieves chunks of HTML and emits Selenium actions; a lightweight navigation engine handles scrolling and tabs. Model comparisons show Gemini 2.5 Pro wins for effectiveness, Claude 3.7 recovers from loops best, Anthropic follows instructions most reliably. Real runs complete an application in nine steps but testing is costly (~$3 per site) and GUI interpretation plus prompt engineering remain the main pain points.
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chapters (14)
- 0:00Introduction
- 0:51Large Action Models and LLM agents
- 2:58Autonomous browsing approaches
- 4:01Job application use case
- 4:46Browsers need agent access
- 6:52Browser automation frameworks
- 7:33Lavash vs Browser Use
- 9:18Autonomous browsing architecture
- 12:25Prompt engineering for agents
- 14:13Prompt structure example
- 17:44Model performance comparison
- 20:34Project lessons learned
- 22:17Live demo
- 25:03Q&A
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