Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
<|reference_start|>Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems: 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.<|reference_end|>
@article{abuelsaad2024agent-e:,
    title={Agent-E: From Autonomous Web Navigation to Foundational Design
  Principles in Agentic Systems},
    author={Tamer Abuelsaad and Deepak Akkil and Prasenjit Dey and Ashish Jagmohan
  and Aditya Vempaty and Ravi Kokku},
    journal={arXiv preprint arXiv:2407.13032},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13032},
    primaryClass={cs.AI}
}