What are the five principles to keep in mind for AI agent implementation and future integration?
More than half of executives at large companies say they plan to implement AI agents in the near future

In a survey of executives at large companies, 10% of organizations have already implemented AI agents, and more than half say they plan to do so soon.
The technology differs from traditional/generative AI in that it is specialized for specific tasks, operates autonomously, and has decision-making capabilities.

AI agents are attracting attention because, in addition to automating routine tasks, they bring a variety of benefits, including streamlining and optimizing decision-making, reducing lead time to product release, and creating innovation.
As with other AI technologies, data is the source of these benefits, and the quality of the data and learning models directly affects the quality of the products produced by AI agents.

In implementing AI agents, it is important to ensure not only data accuracy, but also efficiency and governance.
Unlike AI/generative AI that uses general-purpose data, learning models based on such data, and LLMs, AI agents handle corporate and personal data directly, and thus require particularly strict measures to ensure governance and data storage methods.

In addition to explaining the basics of AI agents, the report notes that some advanced companies are implementing AI agents on a department-by-department and business-by-business basis, with the expectation that they will be integrated in the future.
In addition, please ask for the 5 key points to keep in mind when implementing, the 3 requirements for data infrastructure, and recommended data platforms.