Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems

• URL: http://arxiv.org/abs/2407.13032v1
• Date: Wed, 17 Jul 2024 21:44:28 GMT
• Title: Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
• Authors: Tamer Abuelsaad, Deepak Akkil, Prasenjit Dey, Ashish Jagmohan, Aditya Vempaty, Ravi Kokku,
• Abstract summary: We present our work on building a novel web agent, Agent-E. Agent-E introduces numerous architectural improvements over prior state-of-the-art web agents. We show that Agent-E beats other SOTA text and multi-modal web agents on this benchmark in most categories by 10-30%.
• Score: 1.079505444748609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI Agents are changing the way work gets done, both in consumer and enterprise domains. However, the design patterns and architectures to build highly capable agents or multi-agent systems are still developing, and the understanding of the implication of various design choices and algorithms is still evolving. In this paper, we present our work on building a novel web agent, Agent-E \footnote{Our code is available at \url{https://github.com/EmergenceAI/Agent-E}}. Agent-E introduces numerous architectural improvements over prior state-of-the-art web agents such as hierarchical architecture, flexible DOM distillation and denoising method, and the concept of \textit{change observation} to guide the agent towards more accurate performance. We first present the results of an evaluation of Agent-E on WebVoyager benchmark dataset and show that Agent-E beats other SOTA text and multi-modal web agents on this benchmark in most categories by 10-30\%. We then synthesize our learnings from the development of Agent-E into general design principles for developing agentic systems. These include the use of domain-specific primitive skills, the importance of distillation and de-noising of environmental observations, the advantages of a hierarchical architecture, and the role of agentic self-improvement to enhance agent efficiency and efficacy as the agent gathers experience.

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