This episode explores Agent-E, a new text-only web agent that enhances web task performance through its hierarchical design. The planner agent breaks down user requests into subtasks, while the browser navigation agent executes them using various Python-based skills like clicking or typing. Agent-E intelligently distills webpage content (DOM) to focus on essential information, using methods like text-only, input fields, or all fields, depending on the task. Real-time feedback allows the agent to adapt and correct errors as it works, similar to human learning.Agent-E significantly improves on previous agents like WebVoyager and Wilbur, achieving a 73.2% task success rate, a notable improvement in task efficiency and error awareness. Evaluated across 15 popular websites, it adapts based on task difficulty and requires around 25 LLM calls per task. Beyond web automation, Agent-E's design principles—such as hierarchical task structures, skill modularity, and human-in-the-loop feedback—make it a promising model for future AI agents in areas like desktop automation and robotics. The episode emphasizes the potential for these innovations to extend across various domains, improving AI agent capabilities and efficiency.
https://arxiv.org/pdf/2407.13032