Q&A: What is agentic AI today, and what do we want it to be?
- Agentic AI refers to AI systems that can take actions in the world, such as booking flights or performing robotic tasks.
- A report found that 35% of surveyed businesses had deployed AI agents, with another 44% planning to implement them soon.
- The development of agentic AI faces challenges due to a lack of training data for specific tasks.
- Coding agents are a promising application of agentic AI, allowing them to learn through trial and error.
- There are risks associated with using AI agents, including the potential for bugs and data leaks.
Agentic AI is defined as AI that can take actions in the world, including both physical actions like robotic manipulation and digital actions such as booking flights. This contrasts with generative AI, which focuses on creating content rather than taking direct actions.
According to a report by the MIT Sloan School of Management and Boston Consulting Group, 35% of businesses surveyed have already deployed AI agents, while an additional 44% plan to implement them in the near future. This rapid adoption highlights the growing interest in and reliance on agentic AI technologies.
The development of agentic AI is challenged by the scarcity of training data necessary for specific tasks. For instance, creating an AI system capable of booking a flight requires extensive data on user interactions with airline websites.
Coding agents have shown significant promise as they can learn to solve coding problems through feedback loops, allowing them to improve their performance over time. However, there are inherent risks, such as the introduction of bugs and the potential for private data leaks, which need to be carefully managed as these technologies are integrated into various applications.
问答：今天的代理AI是什么，我们希望它成为什么？
代理AI被定义为能够在现实世界中采取行动的AI，包括物理行动（如机器人操作）和数字行动（如预订航班）。这与生成AI形成对比，后者专注于创建内容而不是直接采取行动。
根据麻省理工学院斯隆管理学院和波士顿咨询集团的一份报告，35%的受访企业已经部署了AI代理，另有44%计划在不久的将来实施。这一快速采用凸显了对代理AI技术日益增长的兴趣和依赖。
代理AI的发展面临着特定任务所需训练数据匮乏的挑战。例如，创建一个能够预订航班的AI系统需要大量关于用户与航空公司网站互动的数据。
编码代理显示出显著的前景，因为它们可以通过反馈循环学习解决编码问题，从而随着时间的推移提高其性能。然而，存在固有的风险，例如引入错误和潜在的私人数据泄露，这些都需要在将这些技术整合到各种应用中时进行仔细管理。