In recent advancements within the domain of Large Language Models (LLMs), there has been a notable emergence of agents capable of addressing Robotic Process Automation (RPA) challenges through enhanced cognitive capabilities and sophisticated reasoning. This development heralds a new era of scalability and human-like adaptability in goal attainment. In this context, we introduce AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical techniques to facilitate autonomous navigation and task execution on web interfaces, thereby obviating the necessity for predefined scripts or manual intervention. Our engine empowers agents to comprehend and implement complex workflows, adapting to dynamic web environments with unparalleled efficiency. Our methodology synergizes cognitive functionalities with robotic automation, endowing AUTONODE with the ability to learn from experience. We have integrated an exploratory module, DoRA (Discovery and mapping Operation for graph Retrieval Agent), which is instrumental in constructing a knowledge graph that the engine utilizes to optimize its actions and achieve objectives with minimal supervision. The versatility and efficacy of AUTONODE are demonstrated through a series of experiments, highlighting its proficiency in managing a diverse array of web-based tasks, ranging from data extraction to transaction processing.

AUTONODE是一个具备增强认知能力和复杂推理能力的代理系统，通过应用先进的神经图技术在网络界面上实现自主导航和任务执行，无需预定义脚本或手动干预，从而实现了对动态网络环境的高效自适应。该系统通过与机器人自动化相结合的方式，赋予了从经验中学习的能力，并通过探索模块DoRA构建知识图，以最小的监督实现目标优化。一系列实验证明了AUTONODE在处理从数据提取到交易处理等多样化网络任务方面的熟练能力。

AUTONODE: 一个认知 GUI 自动化的神经图形自学引擎