基于代理的多模式人工智能系统正在成为我们日常生活中无处不在的存在。使这些系统更具交互性的一个有前景的方向是将它们体现为特定环境中的代理。将大型基础模型作为特定环境中的代理，可以提供一种将视觉和上下文信息整合到具体系统中的方法。交互性Agent AI是一个交互式系统，可以感知视觉刺激、语言输入或其他基于环境的数据，并可以产生有意义的动作、操纵、导航、手势等。

Agent-based multimodal AI systems are becoming a ubiquitous presence in our everyday lives. A promising direction for making these systems more interactive is to embody them as agents within specific environments. The grounding of large foundation models to act as agents within specific environments can provide a way of incorporating visual and contextual information into an embodied system. Emergent Agent AI as an interactive system that can perceive visual stimuli, language inputs, or other environmentally-grounded data and can produce meaningful actions, manipulation, navigation, gesture, etc. 

在人工智能中，代理是一种计算机程序或系统，旨在感知其环境，做出决策并采取行动以实现特定目标或一组目标。智能体自主运行，这意味着它不受人类操作员的直接控制。

代理可以根据其特征分为不同的类型，例如它们是被动的还是主动的，它们是具有固定的还是动态的环境，以及它们是单代理系统还是多代理系统。

In artificial intelligence, an agent is a computer program or system that is designed to perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.

Agents can be classified into different types based on their characteristics, such as whether they are reactive or proactive, whether they have a fixed or dynamic environment, and whether they are single or multi-agent systems.

本所致力于研究通过AI Agent解决工程领域人工成本高、工程师和工人学习效率低、周期长、经验无法快速传承的痛点难点，从而实现工程企业生产力质的飞跃。

Our research institute is committed to studying the use of AI agents to solve the pain points and difficulties in the engineering field, such as high labor costs, low learning efficiency for engineers and workers, long cycles, and inability to quickly pass on experience, in order to achieve a qualitative leap in productivity for engineering enterprises.